{"id":1425,"date":"2026-05-24T15:29:31","date_gmt":"2026-05-24T15:29:31","guid":{"rendered":"https:\/\/lean-app.com\/?p=1425"},"modified":"2026-08-22T18:00:19","modified_gmt":"2026-08-22T18:00:19","slug":"lean-vs-noom","status":"publish","type":"post","link":"https:\/\/lean-app.com\/en\/lean-vs-noom\/","title":{"rendered":"Lean vs Noom: psychological coaching vs metabolic precision"},"content":{"rendered":"<link rel=\"preload\" as=\"image\" href=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-screen_depense.webp\" fetchpriority=\"low\">\n<link rel=\"preload\" as=\"image\" href=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-screen_bilan.webp\" fetchpriority=\"low\">\n<link rel=\"preload\" as=\"image\" href=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-screen_kcal.webp\" fetchpriority=\"low\">\n<link rel=\"preload\" as=\"image\" href=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-screen_strategie.webp\" fetchpriority=\"low\">\n<link 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18px;border-radius:14px;color:#fff;font-family:var(--font-display);font-weight:500;font-size:15px;letter-spacing:-.01em;display:flex;justify-content:space-between;align-items:center;box-shadow:0 6px 18px rgba(0,0,0,.06)}\n#lvm-shell .pyramid .level .k{font-family:var(--font-mono);font-size:10px;text-transform:uppercase;letter-spacing:.08em;opacity:.75}\n#lvm-shell .pyramid .l1{background:#0E0E10;width:100%}\n#lvm-shell .pyramid .l2{background:#1D1D1F;width:84%}\n#lvm-shell .pyramid .l3{background:#3a3a3c;width:68%}\n#lvm-shell .pyramid .l4{background:var(--pink);width:52%}\n#lvm-shell .pyramid-cap{text-align:center;font-size:13px;color:var(--muted);margin-top:14px}\n\n\/* Section 7 honnetete : scorecard horizontal bars *\/\n#lvm-shell .scorecard{margin:30px 0 10px;border:1px solid var(--rule);border-radius:20px;padding:28px 26px;background:#fff}\n#lvm-shell .scorecard-head{display:grid;grid-template-columns:1.4fr 1fr 1fr;column-gap:28px;align-items:center;padding-bottom:18px;margin-bottom:8px;border-bottom:1px solid var(--rule-soft)}\n#lvm-shell .scorecard-head .h-crit{font-family:var(--font-mono);font-size:11px;font-weight:500;text-transform:uppercase;letter-spacing:.08em;color:var(--muted)}\n#lvm-shell .scorecard-head .h-brand{display:flex;align-items:center;gap:8px;font-family:var(--font-display);font-size:14px;font-weight:600;color:var(--ink)}\n#lvm-shell .scorecard-head .h-brand img{width:22px;height:22px;border-radius:5px;object-fit:cover}\n#lvm-shell .scorecard-row{display:grid;grid-template-columns:1.4fr 1fr 1fr;column-gap:28px;align-items:center;padding:14px 0;border-bottom:1px solid var(--rule-soft)}\n#lvm-shell .scorecard-row:last-child{border-bottom:0}\n#lvm-shell .scorecard-row .crit{font-size:14px;color:var(--ink);font-weight:500;padding-right:14px}\n#lvm-shell .scorecard-row .bar{display:flex;flex-direction:row-reverse;align-items:center;gap:10px}\n#lvm-shell .scorecard-row .bar .b{flex:1;height:8px;border-radius:99px;background:var(--rule-soft);overflow:hidden;position:relative}\n#lvm-shell .scorecard-row .bar .b > i{display:block;height:100%;border-radius:99px;transition:width 1s cubic-bezier(.22,.61,.36,1)}\n#lvm-shell .scorecard-row .bar.lean .b > i{background:var(--pink)}\n#lvm-shell .scorecard-row .bar.mfp .b > i{background:var(--mfp)}\n#lvm-shell .scorecard-row .bar .v{font-family:var(--font-mono);font-size:12px;font-weight:600;color:var(--ink);min-width:32px;text-align:left}\n\n\/* Section 8 pour qui : persona checklist *\/\n#lvm-shell .persona{margin:28px 0 10px;display:grid;grid-template-columns:1fr;gap:14px}\n#lvm-shell .persona-it{display:grid;grid-template-columns:54px 1fr;gap:16px;padding:22px 24px;background:#fff;border:1px solid var(--rule);border-radius:18px;align-items:center}\n#lvm-shell .persona-it.match{background:var(--pink-soft);border-color:rgba(255,45,110,.25)}\n#lvm-shell .persona-it .pic{width:54px;height:54px;border-radius:50%;display:flex;align-items:center;justify-content:center;background:var(--rule-soft);position:relative;font-family:var(--font-mono);font-size:13px;font-weight:600;color:var(--ink)}\n#lvm-shell .persona-it.match .pic{background:var(--pink);color:#fff}\n#lvm-shell .persona-it .pic svg{width:24px;height:24px}\n#lvm-shell .persona-it h4{margin:0 0 4px;font-size:17px;letter-spacing:-.01em}\n#lvm-shell .persona-it p{margin:0;font-size:14px;color:var(--muted);line-height:1.55}\n#lvm-shell .persona-it.match h4{color:var(--ink)}\n\n\/* Section 9 migration : timeline steps *\/\n#lvm-shell .steps{display:grid;grid-template-columns:repeat(5,1fr);gap:14px;margin:28px 0;position:relative}\n#lvm-shell .steps::before{content:\"\";position:absolute;top:14px;left:7px;right:calc(20% - 18px);height:1px;background:linear-gradient(90deg,var(--pink) 0%,var(--rule-soft) 100%);z-index:0}\n#lvm-shell .step{position:relative;padding-top:24px;z-index:1}\n#lvm-shell .step::before{content:\"\";position:absolute;top:8px;left:0;width:14px;height:14px;border-radius:50%;background:var(--pink);border:3px solid #fff;box-shadow:0 0 0 1px var(--rule)}\n#lvm-shell .step .sn{font-family:var(--font-mono);font-size:11px;color:var(--pink);font-weight:600;letter-spacing:.08em}\n#lvm-shell .step h4{margin:6px 0 6px;font-size:15px;letter-spacing:-.01em}\n#lvm-shell .step p{font-size:13px;color:var(--muted);line-height:1.5;margin:0}\n\n\/* Section 10 debloque : feature stack numbered XL *\/\n#lvm-shell .feat-stack{margin:30px 0 10px;border-top:1px solid var(--rule)}\n#lvm-shell .feat-it{display:grid;grid-template-columns:auto 1fr auto;gap:24px;padding:26px 0;border-bottom:1px solid var(--rule);align-items:center}\n#lvm-shell .feat-it .fn{font-family:var(--font-display);font-size:48px;font-weight:600;color:var(--pink);line-height:1;letter-spacing:-.04em;width:74px}\n#lvm-shell .feat-it .ft{font-family:var(--font-display);font-size:22px;font-weight:600;color:var(--ink);letter-spacing:-.015em;line-height:1.25;margin-bottom:6px}\n#lvm-shell .feat-it .fd{font-size:15px;color:var(--muted);line-height:1.55;margin:0}\n#lvm-shell .feat-it .fc{font-family:var(--font-mono);font-size:11px;text-transform:uppercase;letter-spacing:.08em;color:var(--muted);font-weight:500}\n#lvm-shell .feat-it:last-child{border-bottom:0}\n\n#lvm-shell .faq{margin:22px 0}\n#lvm-shell .faq details{border-bottom:1px solid var(--rule);padding:20px 0}\n#lvm-shell .faq details:first-of-type{border-top:1px solid var(--rule)}\n#lvm-shell .faq summary{cursor:pointer;list-style:none;display:flex;justify-content:space-between;align-items:center;gap:18px;font-family:var(--font-display);font-size:20px;font-weight:500;letter-spacing:-.015em;color:var(--ink)}\n#lvm-shell .faq summary::-webkit-details-marker{display:none}\n#lvm-shell .faq summary::after{content:\"+\";font-size:24px;color:var(--muted);font-weight:300;line-height:1;transition:transform .25s, color .25s}\n#lvm-shell .faq details[open] summary::after{transform:rotate(45deg);color:var(--pink)}\n#lvm-shell .faq details[open] summary{color:var(--pink)}\n#lvm-shell .faq .ans{margin-top:14px;font-size:16px;color:var(--muted);line-height:1.65}\n\n#lvm-shell .get-band{background:var(--paper-2);border-radius:24px;padding:48px 36px;margin:60px 0 40px;text-align:center}\n#lvm-shell .get-band .kicker{font-family:var(--font-mono);font-size:11px;text-transform:uppercase;color:var(--pink);font-weight:600;letter-spacing:.1em;margin-bottom:14px}\n#lvm-shell .get-band h3{font-size:36px;margin:0 0 14px;letter-spacing:-.025em}\n#lvm-shell .get-band p{font-size:16px;color:var(--muted);max-width:480px;margin:0 auto 26px}\n#lvm-shell .get-band .stores{display:flex;justify-content:center;gap:14px;flex-wrap:wrap}\n#lvm-shell .get-band .stores a{line-height:0;transition:transform .15s}\n#lvm-shell .get-band .stores a:hover{transform:translateY(-3px)}\n#lvm-shell .get-band .stores img{height:60px;width:auto;border-radius:11px}\n\n#lvm-shell .sources{font-size:14px;color:var(--muted);line-height:1.7}\n#lvm-shell .sources ol{padding-left:22px}\n#lvm-shell .sources li{margin-bottom:8px}\n\n#lvm-shell footer{padding:50px 0 60px;border-top:1px solid var(--rule);margin-top:40px}\n#lvm-shell footer .row{display:flex;justify-content:space-between;align-items:center;gap:18px;flex-wrap:wrap}\n#lvm-shell footer .kicker{font-family:var(--font-mono);font-size:11px;text-transform:uppercase;letter-spacing:.08em;color:var(--pink);font-weight:600}\n#lvm-shell footer p{font-size:13px;color:var(--muted);margin:8px 0 0}\n#lvm-shell footer .stores{display:flex;gap:8px}\n#lvm-shell footer .stores img{height:34px;width:auto;border-radius:6px}\n\n#lvm-shell .rev{opacity:0;transform:translateY(12px);transition:opacity .8s cubic-bezier(.22,.61,.36,1),transform .8s cubic-bezier(.22,.61,.36,1)}\n#lvm-shell .rev.on{opacity:1;transform:translateY(0)}\n@media (prefers-reduced-motion:reduce){#lvm-shell .rev{transition:none;opacity:1;transform:none}}\n\n@media (max-width:760px){\n  #lvm-shell .nav-row{padding:8px 18px;gap:8px}\n  #lvm-shell .nav-link{display:none}\n  #lvm-shell .nav-stores img{height:24px}\n  #lvm-shell .wrap{padding:0 22px}\n  #lvm-shell .hero{padding:34px 0 0}\n  #lvm-shell h1{font-size:46px;letter-spacing:-.035em}\n  #lvm-shell h1 .alt{font-size:.55em;margin-top:10px}\n  #lvm-shell .dek{font-size:20px}\n  #lvm-shell .hero-stores img{height:42px}\n  #lvm-shell .hero-bottom{grid-template-columns:1fr;gap:28px;margin:30px 0 40px;padding-top:24px;align-items:stretch}\n  #lvm-shell .phone-wrap{order:-1}\n  #lvm-shell .phone{width:240px}\n  #lvm-shell .tap-hint.desktop{display:none}\n  #lvm-shell .tap-hint.mobile{display:block;position:relative;left:auto;top:auto;text-align:center;margin:0 auto 10px;width:100%}\n  #lvm-shell .tap-hint.mobile .th-arrow{position:relative;display:block;margin:6px auto 0;width:34px;height:34px;transform:none;color:var(--pink)}\n  #lvm-shell .snippet{padding:24px 22px}\n  #lvm-shell .snippet p{font-size:18px}\n  #lvm-shell section{padding:48px 0}\n  #lvm-shell h2{font-size:34px;letter-spacing:-.03em}\n  #lvm-shell h3{font-size:24px}\n  #lvm-shell .section-label{margin-bottom:22px}\n  #lvm-shell .statement{padding:24px 0;margin:32px 0}\n  #lvm-shell .statement .num{font-size:44px}\n  #lvm-shell .statement .lbl{font-size:19px}\n  #lvm-shell .fig{padding:20px 14px 14px;border-radius:16px}\n  #lvm-shell .cv-wrap{height:310px}\n  #lvm-shell .method{grid-template-columns:1fr;gap:20px}\n  #lvm-shell .method.flip{grid-template-columns:1fr}\n  #lvm-shell .method.flip .m-phone{order:0}\n  #lvm-shell .mini-row{grid-template-columns:repeat(3,1fr);gap:10px}\n  #lvm-shell .mini-phone{padding:3px;border-radius:18px;border-width:1px;max-width:110px}\n  #lvm-shell .mini-phone .notch{width:42px;height:11px;border-radius:0 0 8px 8px}\n  #lvm-shell .mini-phone .scr{border-radius:15px}\n  #lvm-shell .mini-cap{font-size:10px}\n  #lvm-shell .mini-cap strong{font-size:13px}\n  #lvm-shell .duo-row{grid-template-columns:repeat(2,1fr);gap:12px}\n  #lvm-shell .duo-row .mini-phone{max-width:130px}\n  #lvm-shell .steps{grid-template-columns:1fr;gap:18px}\n  #lvm-shell .steps::before{display:none}\n  #lvm-shell .step{padding-top:0;padding-left:24px}\n  #lvm-shell .step::before{top:6px;left:0}\n  #lvm-shell .table-row{grid-template-columns:1.4fr .9fr .9fr}\n  #lvm-shell .table-row > .crit{padding:13px 12px;font-size:13px}\n  #lvm-shell .table-row > .cell{padding:13px 10px;font-size:12px;gap:8px}\n  #lvm-shell .table-row.head > div{padding:14px 12px;font-size:10px;gap:7px}\n  #lvm-shell .table-row.head .brand-cell img{width:20px;height:20px}\n  #lvm-shell .get-band{padding:36px 22px;border-radius:18px;margin:40px 0 30px}\n  #lvm-shell .get-band h3{font-size:28px}\n  #lvm-shell .get-band .stores img{height:50px}\n  #lvm-shell .cta-band{padding:22px;gap:14px}\n  #lvm-shell .cta-band .l{font-size:16px;min-width:0}\n  #lvm-shell .cta-band .stores img{height:38px}\n  #lvm-shell .faq summary{font-size:18px;gap:14px}\n  #lvm-shell .pyramid{max-width:100%}\n  #lvm-shell .pyramid .level{padding:11px 14px;font-size:14px}\n  #lvm-shell .scorecard{padding:20px 16px;border-radius:16px}\n  #lvm-shell .scorecard-head{grid-template-columns:1.2fr 1fr 1fr;column-gap:14px}\n  #lvm-shell .scorecard-head .h-brand{font-size:12px;gap:5px}\n  #lvm-shell .scorecard-head .h-brand img{width:18px;height:18px}\n  #lvm-shell .scorecard-row{grid-template-columns:1.2fr 1fr 1fr;column-gap:14px;padding:12px 0}\n  #lvm-shell .scorecard-row .crit{font-size:13px;padding-right:8px}\n  #lvm-shell .scorecard-row .bar{gap:6px}\n  #lvm-shell .scorecard-row .bar .v{font-size:11px;min-width:26px}\n  #lvm-shell .persona-it{grid-template-columns:44px 1fr;gap:12px;padding:16px 16px;border-radius:14px}\n  #lvm-shell .persona-it .pic{width:44px;height:44px;font-size:12px}\n  #lvm-shell .persona-it h4{font-size:15px}\n  #lvm-shell .persona-it p{font-size:13px}\n  #lvm-shell .feat-it{grid-template-columns:auto 1fr;gap:14px;padding:20px 0}\n  #lvm-shell .feat-it .fn{font-size:36px;width:54px}\n  #lvm-shell .feat-it .ft{font-size:18px}\n  #lvm-shell .feat-it .fd{font-size:13px}\n  #lvm-shell .feat-it .fc{display:none}\n}\n@media (max-width:480px){\n  #lvm-shell .phone-tabs{gap:5px}\n  #lvm-shell .phone-tabs button{padding:5px 8px;font-size:10px}\n  #lvm-shell .nav-stores{gap:4px}\n  #lvm-shell .nav-stores img{height:22px}\n  #lvm-shell .hero-stores img{height:40px}\n  #lvm-shell .crumb{font-size:12px}\n  #lvm-shell .table-row{grid-template-columns:1.3fr .85fr .85fr}\n  #lvm-shell .table-row > .crit{padding:11px 9px;font-size:12px}\n  #lvm-shell .table-row > .cell{padding:11px 8px;font-size:11px;gap:6px}\n  #lvm-shell .table-row.head > div{padding:11px 9px;font-size:9px;gap:5px}\n}<\/style>\n\n<style id=\"lvm-collision-reset\">\n\/* Hard reset for global theme styles that collide with our content *\/\nbody.postid-1425 #lvm-shell .hero{display:block!important;align-items:initial!important;justify-content:initial!important;text-align:left!important;flex-direction:initial!important;padding:54px 0 0!important}\nbody.postid-1425 #lvm-shell .wrap,\nbody.postid-1425 #lvm-shell main.wrap{display:block!important;max-width:760px!important;margin-left:auto!important;margin-right:auto!important;padding-left:28px!important;padding-right:28px!important}\n@media (max-width:820px){\n  body.postid-1425 #lvm-shell .wrap,\n  body.postid-1425 #lvm-shell main.wrap{padding-left:18px!important;padding-right:18px!important}\n}\nhtml, body{overflow-x:hidden!important}\nbody.postid-1425 #lvm-shell{overflow-x:hidden;max-width:100vw}\nbody.postid-1425 #lvm-shell *{max-width:100%}\nbody.postid-1425 #lvm-shell .nav-row{max-width:100vw;box-sizing:border-box}\nbody.postid-1425 #lvm-shell.force-show .rev{opacity:1!important;transform:none!important}\n\n\/* === A.1 PHONE BACKGROUND CLASSES === *\/\nbody.postid-1425 #lvm-shell .phone-bg{position:absolute;inset:0;width:100%;height:100%;background-size:cover;background-position:center top;background-repeat:no-repeat;transition:opacity .28s ease;background-color:#FAF0E6}\nbody.postid-1425 #lvm-shell .phone-bg.tab-depense{background-image:url(https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-screen_depense.webp)}\nbody.postid-1425 #lvm-shell .phone-bg.tab-bilan{background-image:url(https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-screen_bilan.webp)}\nbody.postid-1425 #lvm-shell .phone-bg.tab-kcal{background-image:url(https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-screen_kcal.webp)}\nbody.postid-1425 #lvm-shell .phone-bg.tab-strategie{background-image:url(https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-screen_strategie.webp)}\nbody.postid-1425 #lvm-shell .phone-bg.sub-BMR{background-image:url(https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-screen_BMR.webp)}\nbody.postid-1425 #lvm-shell .phone-bg.sub-NEAT{background-image:url(https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-screen_NEAT.webp)}\nbody.postid-1425 #lvm-shell .phone-bg.sub-EAT{background-image:url(https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-screen_EAT.webp)}\nbody.postid-1425 #lvm-shell .phone-bg.sub-TEF{background-image:url(https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-screen_TEF.webp)}\n\n\/* === v11.3 CTA BANDS MOBILE (badges plus gros + centrage) === *\/\n@media (max-width:760px){\n  body.postid-1425 #lvm-shell .cta-band{flex-direction:column!important;align-items:center!important;text-align:center!important;padding:26px 22px!important;gap:20px!important}\n  body.postid-1425 #lvm-shell .cta-band .l{min-width:0!important;width:100%!important;font-size:16px!important;line-height:1.5!important;text-align:center!important}\n  body.postid-1425 #lvm-shell .cta-band .stores{width:100%!important;justify-content:center!important;flex-wrap:nowrap!important;gap:10px!important}\n  body.postid-1425 #lvm-shell .cta-band .stores a{flex:1!important;max-width:170px!important;display:flex!important;justify-content:center!important}\n  body.postid-1425 #lvm-shell .cta-band .stores picture{width:100%!important;display:block!important}\n  body.postid-1425 #lvm-shell .cta-band .stores img{height:56px!important;width:100%!important;max-width:170px!important;object-fit:contain!important;object-position:center!important;border-radius:10px!important}\n  body.postid-1425 #lvm-shell .get-band{padding:38px 22px!important}\n  body.postid-1425 #lvm-shell .get-band .stores{justify-content:center!important;flex-wrap:nowrap!important;gap:10px!important}\n  body.postid-1425 #lvm-shell .get-band .stores a{flex:1!important;max-width:170px!important;display:flex!important;justify-content:center!important}\n  body.postid-1425 #lvm-shell .get-band .stores picture{width:100%!important;display:block!important}\n  body.postid-1425 #lvm-shell .get-band .stores img{height:56px!important;width:100%!important;max-width:170px!important;object-fit:contain!important;object-position:center!important;border-radius:10px!important}\n  body.postid-1425 #lvm-shell .get-band h3{font-size:26px!important;line-height:1.2!important}\n  body.postid-1425 #lvm-shell .get-band p{font-size:15px!important}\n}\n\n\/* === v11.2 BRAND BANNER above table responsive === *\/\n@media (max-width:760px){\n  body.postid-1425 #lvm-shell .brand-banner img{width:54px!important;height:54px!important}\n  body.postid-1425 #lvm-shell .brand-banner > div{padding:16px 12px!important;gap:8px!important}\n  body.postid-1425 #lvm-shell .brand-banner > div > div{font-size:15px!important}\n}\n\n\/* === v11.4 SCORECARD partie 7: redesign mobile === *\/\n@media (max-width:760px){\n  body.postid-1425 #lvm-shell .scorecard{padding:18px 16px!important;border-radius:16px!important}\n  body.postid-1425 #lvm-shell .scorecard-head{display:none!important}\n  body.postid-1425 #lvm-shell .scorecard-row{\n    display:block!important;\n    padding:14px 0!important;\n    border-bottom:1px solid #E8E2D6!important;\n  }\n  body.postid-1425 #lvm-shell .scorecard-row .crit{\n    display:block!important;\n    font-size:13px!important;\n    font-weight:600!important;\n    color:#0E0E10!important;\n    margin-bottom:10px!important;\n    padding-right:0!important;\n  }\n  body.postid-1425 #lvm-shell .scorecard-row .bar{\n    display:grid!important;\n    grid-template-columns:54px 1fr 32px!important;\n    column-gap:8px!important;\n    align-items:center!important;\n    padding:5px 0!important;\n    flex-direction:initial!important;\n    position:relative!important;\n  }\n  body.postid-1425 #lvm-shell .scorecard-row .bar::before{\n    content:attr(data-brand)!important;\n    font-family:-apple-system,'SF Pro Display',sans-serif!important;\n    font-size:11px!important;\n    font-weight:600!important;\n    text-transform:uppercase!important;\n    letter-spacing:.05em!important;\n    color:#0E0E10!important;\n  }\n  body.postid-1425 #lvm-shell .scorecard-row .bar.lean::before{color:#FF2D6E!important}\n  body.postid-1425 #lvm-shell .scorecard-row .bar.mfp::before{color:#5B7FFF!important}\n  body.postid-1425 #lvm-shell .scorecard-row .bar .b{\n    height:10px!important;\n    width:100%!important;\n    border-radius:99px!important;\n    position:relative!important;\n    background:#EFEAE0!important;\n    overflow:hidden!important;\n  }\n  body.postid-1425 #lvm-shell .scorecard-row .bar .b > i{\n    display:block!important;\n    height:100%!important;\n    border-radius:99px!important;\n  }\n  body.postid-1425 #lvm-shell .scorecard-row .bar .v{\n    font-family:-apple-system,'SF Pro Display',sans-serif!important;\n    font-size:12px!important;\n    font-weight:700!important;\n    color:#0E0E10!important;\n    min-width:0!important;\n    text-align:right!important;\n  }\n}\n\n\/* === A.2 CHARTS MOBILE === *\/\n@media (max-width:760px){\n  \/* v13: charts FULL WIDTH (less card padding) + plus hauts pour vraie respiration *\/\n  body.postid-1425 #lvm-shell .cv-wrap{height:380px!important;min-height:360px!important;max-height:420px!important;width:100%!important}\n  body.postid-1425 #lvm-shell .cv-wrap canvas{width:100%!important;height:100%!important;display:block!important}\n  body.postid-1425 #lvm-shell .fig{padding:16px 4px 14px!important;margin:24px -4px 14px!important;overflow:visible!important}\n  body.postid-1425 #lvm-shell .fig-head{padding:0 12px!important;flex-wrap:wrap!important;gap:6px!important;margin-bottom:10px!important}\n  body.postid-1425 #lvm-shell .fig-body{padding:0 2px!important}\n  body.postid-1425 #lvm-shell .fig-cap{padding:0 12px!important;font-size:13px!important;margin-top:10px!important}\n}\n@media (max-width:480px){\n  body.postid-1425 #lvm-shell .cv-wrap{height:360px!important;min-height:340px!important;max-height:380px!important}\n  body.postid-1425 #lvm-shell .fig{padding:14px 2px 12px!important;margin:20px -6px 12px!important;border-radius:14px!important}\n  body.postid-1425 #lvm-shell .fig-body{padding:0!important}\n}\n\n\/* === v11.2 TABLEAU MOBILE STACKED CARDS avec mini-tags Lean\/MFP === *\/\n@media (max-width:760px){\n  body.postid-1425 #lvm-shell .table{border-radius:14px!important}\n  body.postid-1425 #lvm-shell .table-row.head{display:none!important}\n  body.postid-1425 #lvm-shell .table-row{\n    display:grid!important;\n    grid-template-columns:1fr 1fr!important;\n    grid-template-areas:\"crit crit\" \"lean mfp\"!important;\n    gap:0!important;\n    min-height:0!important;\n  }\n  body.postid-1425 #lvm-shell .table-row > .crit{\n    grid-area:crit!important;background:#0E0E10!important;color:#fff!important;\n    padding:11px 14px!important;font-size:13px!important;font-weight:600!important;\n    letter-spacing:-0.1px!important;border-right:0!important;line-height:1.35!important;\n    font-family:-apple-system,BlinkMacSystemFont,'SF Pro Display',sans-serif!important;text-transform:none!important;\n  }\n  body.postid-1425 #lvm-shell .table-row > .cell.lean{\n    grid-area:lean!important;border-right:1px solid #E8E2D6!important;\n    position:relative!important;background:#FFF1F5!important;padding-top:30px!important;\n  }\n  body.postid-1425 #lvm-shell .table-row > .cell:not(.lean):not(.crit){\n    grid-area:mfp!important;background:#F5F5F7!important;padding-top:30px!important;\n    position:relative!important;\n  }\n  body.postid-1425 #lvm-shell .table-row > .cell.lean::before{\n    content:\"LEAN\"!important;position:absolute!important;top:8px!important;left:12px!important;\n    right:auto!important;bottom:auto!important;width:auto!important;height:auto!important;\n    background:transparent!important;\n    font-family:-apple-system,'SF Pro Display',sans-serif!important;\n    font-size:10px!important;font-weight:700!important;letter-spacing:.07em!important;\n    color:#FF2D6E!important;\n  }\n  body.postid-1425 #lvm-shell .table-row > .cell:not(.lean):not(.crit)::before{\n    content:\"NOOM\"!important;position:absolute!important;top:8px!important;left:12px!important;\n    font-family:-apple-system,'SF Pro Display',sans-serif!important;\n    font-size:10px!important;font-weight:700!important;letter-spacing:.07em!important;\n    color:#FF6E5E!important;\n  }\n  body.postid-1425 #lvm-shell .table-row > .cell{\n    padding:12px 12px!important;font-size:13px!important;line-height:1.4!important;\n    align-items:flex-start!important;gap:7px!important;\n  }\n  body.postid-1425 #lvm-shell .icn{flex-shrink:0!important;margin-top:1px!important}\n}\n\n\/* === A.5 MINI-LOGOS partie 7 (triplet NEAT\/EAT\/TEF) === *\/\n@media (max-width:760px){\n  body.postid-1425 #lvm-shell .mini-row{gap:6px!important;margin:24px 0!important;grid-template-columns:repeat(3,1fr)!important}\n  body.postid-1425 #lvm-shell .mini-phone{max-width:100px!important;padding:2px!important;border-radius:14px!important;border-width:1px!important}\n  body.postid-1425 #lvm-shell .mini-phone.tiny{max-width:96px!important;padding:2px!important;border-radius:13px!important}\n  body.postid-1425 #lvm-shell .mini-phone .notch{width:30px!important;height:8px!important;border-radius:0 0 5px 5px!important}\n  body.postid-1425 #lvm-shell .mini-phone .scr{border-radius:11px!important}\n  body.postid-1425 #lvm-shell .mini-cap{font-size:10px!important;margin-top:8px!important}\n  body.postid-1425 #lvm-shell .mini-cap strong{font-size:12px!important;margin-top:2px!important}\n}\n\n\/* === MOCKUP TAP HINT MOBILE === *\/\n@media (max-width:760px){\n  body.postid-1425 #lvm-shell .tap-hint.mobile{position:relative!important;width:100%!important;left:auto!important;top:auto!important;text-align:center!important;margin:0 auto 14px!important;display:block!important}\n  body.postid-1425 #lvm-shell .tap-hint.desktop{display:none!important}\n  body.postid-1425 #lvm-shell .tap-hint.hidden{display:none!important;height:0!important;margin:0!important;padding:0!important}\n}\n\n\/* === A.6 BODYSCAN ILLUST partie BMR (override mobile mini-phone) === *\/\nbody.postid-1425 #lvm-shell .bodyscan-illust{margin:40px auto 8px!important;display:flex!important;flex-direction:column!important;align-items:center!important;gap:14px!important;max-width:220px!important}\nbody.postid-1425 #lvm-shell .bodyscan-illust .mini-phone{max-width:200px!important;padding:3px!important;border-radius:22px!important;border-width:1px!important}\nbody.postid-1425 #lvm-shell .bodyscan-illust .mini-phone .notch{width:40px!important;height:11px!important;border-radius:0 0 7px 7px!important}\nbody.postid-1425 #lvm-shell .bodyscan-illust .mini-phone .scr{border-radius:18px!important}\n@media (max-width:760px){\n  body.postid-1425 #lvm-shell .bodyscan-illust{max-width:180px!important}\n  body.postid-1425 #lvm-shell .bodyscan-illust .mini-phone{max-width:160px!important;padding:3px!important;border-radius:20px!important}\n  body.postid-1425 #lvm-shell .bodyscan-illust .mini-phone .notch{width:34px!important;height:9px!important;border-radius:0 0 6px 6px!important}\n  body.postid-1425 #lvm-shell .bodyscan-illust .mini-phone .scr{border-radius:16px!important}\n}\n<\/style>\n\n\n<div id=\"lvm-shell\"><div class=\"progress\" aria-hidden=\"true\"><i id=\"progBar\"><\/i><\/div>\n\n<header class=\"nav\">\n  <div class=\"nav-row\">\n    <a class=\"nav-brand\" href=\"https:\/\/lean-app.com\/en\/\" aria-label=\"Lean home\">\n      <img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-logo-lean-square-scaled.webp\" alt=\"\" width=\"512\" height=\"512\" loading=\"lazy\" decoding=\"async\" \/>\n      <span>Lean<\/span>\n    <\/a>\n    <span class=\"nav-spacer\"><\/span>\n    <a class=\"nav-link\" href=\"https:\/\/lean-app.com\/en\/tdee-calculator\/\">TDEE Calculator<\/a>\n    <div class=\"nav-stores\">\n      <a href=\"https:\/\/apps.apple.com\/fr\/app\/lean-calorie-ai-podometre\/id6738668646\" target=\"_blank\" rel=\"noopener\" aria-label=\"Download on the App Store\">\n        <img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-appstore-official.webp\" alt=\"App Store\" width=\"413\" height=\"122\" loading=\"lazy\" decoding=\"async\" \/>\n      <\/a>\n      <a href=\"https:\/\/play.google.com\/store\/apps\/details?id=com.lean.testsqflite\" target=\"_blank\" rel=\"noopener\" aria-label=\"Available on Google Play\">\n        <img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-googleplay-official.webp\" alt=\"Google Play\" width=\"315\" height=\"95\" loading=\"lazy\" decoding=\"async\" \/>\n      <\/a>\n    <\/div>\n  <\/div>\n<\/header>\n\n<main class=\"wrap\">\n\n<section class=\"hero\" aria-labelledby=\"title\">\n  <div class=\"crumb\"><a href=\"https:\/\/lean-app.com\/en\/\">Home<\/a> &nbsp;\/&nbsp; Lean vs Noom<\/div>\n  <div class=\"eyebrow\">Comparison &middot; Nutrition &amp; TDEE<\/div>\n  <h1 id=\"title\">Lean versus Noom.\n    <span class=\"alt\">Psychological coaching versus metabolic precision.<\/span>\n  <\/h1>\n  <p class=\"dek\">Noom sells behavioral coaching to change your habits. Lean sees your real expenditure. Two promises that don&rsquo;t play on the same field.<\/p>\n  <div class=\"byline\">\n    <img class=\"by-logo\" src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-logo-lean-square-scaled.webp\" alt=\"\" width=\"512\" height=\"512\" loading=\"lazy\" decoding=\"async\" \/>\n    <span><strong>The Lean Team<\/strong> &middot; 12&nbsp;min read &middot; Updated May 24, 2026<\/span>\n  <\/div>\n  <div class=\"hero-stores\">\n    <a href=\"https:\/\/apps.apple.com\/fr\/app\/lean-calorie-ai-podometre\/id6738668646\" target=\"_blank\" rel=\"noopener\">\n      <img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-appstore-official.webp\" alt=\"T\u00e9l\u00e9charger sur l'App Store\" width=\"413\" height=\"122\" loading=\"lazy\" decoding=\"async\" \/>\n    <\/a>\n    <a href=\"https:\/\/play.google.com\/store\/apps\/details?id=com.lean.testsqflite\" target=\"_blank\" rel=\"noopener\">\n      <img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-googleplay-official.webp\" alt=\"Disponible sur Google Play\" width=\"315\" height=\"95\" loading=\"lazy\" decoding=\"async\" \/>\n    <\/a>\n    <span class=\"or\">Free download<\/span>\n  <\/div>\n\n  <div class=\"hero-bottom\">\n    <div class=\"hero-lead\">\n      Noom is known for its long, personalized sign-up quiz, its daily food-psychology lessons, its green\/yellow\/red food classification, and access to a human coach. A real strength for adherence and habit work. But its TDEE formula remains Mifflin-St Jeor 1990, plus a static activity factor you tick once during the sign-up quiz. No real bodyfat measured inside the app, no metabolic adaptation. Over 3 months of a serious cut, the gap widens.\n    <\/div>\n    <div class=\"phone-wrap rev\">\n      <div class=\"phone-stage\">\n        <div class=\"tap-hint mobile\" id=\"tapHintMobile\" aria-hidden=\"true\">\n          <span class=\"th-pill\"><small>Interactive demo<\/small>Tap the screen to explore the app<\/span>\n          <svg class=\"th-arrow\" viewbox=\"0 0 24 24\" fill=\"none\" aria-hidden=\"true\">\n            <path d=\"M12 4 L12 20 M5 13 L12 20 L19 13\" stroke=\"currentColor\" stroke-width=\"2.4\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/>\n          <\/svg>\n        <\/div>\n        <div class=\"tap-hint desktop\" id=\"tapHintDesktop\" aria-hidden=\"true\">\n          <span class=\"th-pill\"><small>Interactive demo<\/small>Tap the screen<br>to explore the app<\/span>\n          <svg class=\"th-arrow\" viewbox=\"0 0 104 34\" fill=\"none\" aria-hidden=\"true\">\n            <path d=\"M4 9 C 34 1, 64 20, 94 27\" stroke=\"currentColor\" stroke-width=\"2.6\" fill=\"none\" stroke-linecap=\"round\"\/>\n            <path d=\"M86 20 L 94 27 L 84 30\" stroke=\"currentColor\" stroke-width=\"2.6\" fill=\"none\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/>\n          <\/svg>\n        <\/div>\n        <div class=\"phone\" id=\"phone\" role=\"img\" aria-label=\"Preview of the Lean app with TDEE drill-down\">\n          <div class=\"notch\"><\/div>\n          <div class=\"phone-screen\">\n            <button class=\"phone-back\" id=\"phoneBack\" aria-label=\"Back\">&#8249;<\/button>\n            <div id=\"phoneImg\" class=\"phone-bg tab-depense\" role=\"img\" aria-label=\"Lean preview, Expenditure tab\"><\/div>\n            <div class=\"phone-zones\" id=\"phoneZones\">\n              <div class=\"z\" data-sub=\"BMR\"  style=\"top:11%;height:21%\" role=\"button\" tabindex=\"0\" aria-label=\"BMR detail\"><\/div>\n              <div class=\"z\" data-sub=\"NEAT\" style=\"top:33%;height:16%\" role=\"button\" tabindex=\"0\" aria-label=\"NEAT detail\"><\/div>\n              <div class=\"z\" data-sub=\"EAT\"  style=\"top:50%;height:16%\" role=\"button\" tabindex=\"0\" aria-label=\"EAT detail\"><\/div>\n              <div class=\"z\" data-sub=\"TEF\"  style=\"top:67%;height:16%\" role=\"button\" tabindex=\"0\" aria-label=\"TEF detail\"><\/div>\n            <\/div>\n            <div class=\"phone-navbar\" id=\"phoneNav\" aria-hidden=\"false\">\n              <button data-tab=\"bilan\"     type=\"button\" aria-label=\"Balance tab\"><\/button>\n              <button data-tab=\"kcal\"      type=\"button\" aria-label=\"Calories tab\"><\/button>\n              <button data-tab=\"depense\"   type=\"button\" aria-label=\"Expenditure tab\"><\/button>\n              <button data-tab=\"strategie\" type=\"button\" aria-label=\"Strategy tab\"><\/button>\n            <\/div>\n          <\/div>\n        <\/div>\n        <div class=\"phone-tabs\" role=\"tablist\" aria-label=\"Navigate the Lean app\">\n          <button data-tab=\"bilan\"     type=\"button\">Balance<\/button>\n          <button data-tab=\"kcal\"      type=\"button\">Calories<\/button>\n          <button data-tab=\"depense\"   type=\"button\" class=\"on\">Expenditure<\/button>\n          <button data-tab=\"strategie\" type=\"button\">Strategy<\/button>\n        <\/div>\n      <\/div>\n    <\/div>\n  <\/div>\n\n  <div class=\"snippet rev\">\n    <div class=\"lbl\">Quick answer<\/div>\n    <p>Noom calculates your TDEE with Mifflin-St Jeor 1990 (no bodyfat measured inside the app) and a static activity factor chosen during the sign-up quiz. Noom&rsquo;s real strength is elsewhere: a personalized quiz that creates strong initial engagement, daily food-psychology lessons, a green\/yellow\/red food classification, and access to a human coach who works on adherence. Lean takes a different stance: recalculate every component of TDEE (<span data-term=\"BMR\">BMR<span class=\"tt\">Basal Metabolic Rate. Energy expended at rest. In Lean, calculated on actual lean mass via BodyScan AI.<\/span><\/span> on real bodyfat via a patented proprietary model, <span data-term=\"NEAT\">NEAT<span class=\"tt\">Non-Exercise Activity Thermogenesis. Expenditure from steps and daily activities outside of sport.<\/span><\/span> from steps, <span data-term=\"EAT\">EAT<span class=\"tt\">Exercise Activity Thermogenesis. Expenditure tied to sport sessions, calculated through MET.<\/span><\/span> via MET, <span data-term=\"TEF\">TEF<span class=\"tt\">Thermic Effect of Food. Energy spent on digestion. Depends on the macros you eat.<\/span><\/span> per macros) and modulate the BMR through metabolic adaptation continuously, with no coefficient to pick.<\/p>\n  <\/div>\n<\/section>\n\n<section aria-labelledby=\"constat\" class=\"rev\">\n  <div class=\"section-label\"><span class=\"bar\"><\/span><span class=\"num\">00 &middot; The reality<\/span><\/div>\n  <h2 id=\"constat\">Noom sells coaching, not your metabolic adaptation<\/h2>\n  <p>If you&rsquo;re reading this, you&rsquo;ve probably already installed Noom. You took the long sign-up quiz, those 20 minutes of very personal questions about your history with weight, your blocks, your emotions, your habits. You felt understood. You entered your weight, your height, your age, your sex, and picked your activity level from a static list. The app showed you a calorie target, say 1,500&nbsp;kcal to lose weight.<\/p>\n  <p>You followed the daily 5 to 10 minute lessons on food psychology. You classified your meals as green, yellow, red. You chatted with your human coach on tough days. For the first 6 weeks, it works. You lose. You&rsquo;re happy. Then around week 8, the scale freezes. You tighten the screws. You drop to 1,350&nbsp;kcal. Still nothing moves.<\/p>\n\n  <div class=\"statement\">\n    <div class=\"num\">&minus;10 to &minus;15&nbsp;%<\/div>\n    <div class=\"lbl\">of measured TDEE decline after 4 to 6 weeks of a &minus;500&nbsp;kcal\/day deficit. Noom doesn&rsquo;t detect it. Your calorie target stays frozen on the activity factor you ticked during the sign-up quiz, 100&nbsp;days ago.<\/div>\n  <\/div>\n\n  <p>Imagine Noom shows you a TDEE of 2,000&nbsp;kcal. You eat 1,500 (theoretical deficit of 500&nbsp;kcal). But in reality, your <a class=\"inline\" href=\"https:\/\/lean-app.com\/en\/depense-energetique-totale-v2\/\">TDEE has dropped to 1,700&nbsp;kcal<\/a> due to metabolic adaptation. You&rsquo;re only at a 200&nbsp;kcal real deficit, not 500. Loss slows drastically. No daily Noom lesson can fix that, because the problem isn&rsquo;t in your head, it&rsquo;s in the equation.<\/p>\n  <p>The Noom promise is clear and delivered on its behavioral side: you feel supported, you work on your emotional triggers, you learn to classify the quality of your choices. It&rsquo;s valuable for adherence. What Noom doesn&rsquo;t do is <a class=\"inline\" href=\"https:\/\/lean-app.com\/en\/comment-compter-ses-calories\/\">recompute your expenditure<\/a> as weeks of deficit go by. And that's exactly where the \"calorie tracker\" promise stops, even though it's the lever that actually drives weight loss.<\/p>\n<\/section>\n\n<section aria-labelledby=\"p1\" class=\"rev\">\n  <div class=\"section-label\"><span class=\"bar\"><\/span><span class=\"num\">01 &middot; Problem 1<\/span><\/div>\n  <h2 id=\"p1\">The 1990 BMR formula, with no bodyfat measured in the app<\/h2>\n  <p>Derri\u00e8re les cours quotidiens et la classification des aliments, Noom doit bien poser un chiffre&nbsp;: ton m\u00e9tabolisme de base, l&rsquo;\u00e9nergie br\u00fbl\u00e9e au repos. Il l&rsquo;obtient avec l&rsquo;\u00e9quation de Mifflin-St Jeor, celle qu&rsquo;utilise l&rsquo;\u00e9crasante majorit\u00e9 des trackers grand public.<\/p>\n  <p>Mifflin-St Jeor date de 1990. Sur le papier c&rsquo;est un progr\u00e8s&nbsp;: 498 sujets, calorim\u00e9trie indirecte, population plus repr\u00e9sentative que les travaux de 1919. Noom l&rsquo;applique telle quelle, sans variante.<\/p>\n  <p>Certains concurrents proposent au moins une porte de sortie, l&rsquo;\u00e9quation Katch-McArdle, qui travaille sur la masse maigre si tu saisis ton taux de masse grasse. Noom n&rsquo;offre pas cette option&nbsp;: pas de champ bodyfat, pas de calcul alternatif. Le coaching s&rsquo;appuie donc sur une estimation que rien ne vient corriger.<\/p>\n  <p>Le progr\u00e8s de 1990 sur 1919 est r\u00e9el mais marginal, car le d\u00e9faut de fond ne bouge pas&nbsp;: l&rsquo;\u00e9quation ne conna\u00eet que ton poids. Ni ton bodyfat, ni ta masse maigre.<\/p>\n  <p>Or la masse grasse consomme tr\u00e8s peu d&rsquo;\u00e9nergie au repos. Ce sont les organes et les muscles qui d\u00e9pensent&nbsp;: le foie, le cerveau, le c\u0153ur, les reins. Deux corps de m\u00eame poids avec des compositions diff\u00e9rentes n&rsquo;ont donc pas le m\u00eame m\u00e9tabolisme.<\/p>\n  <p>Frankenfield 2013 (PubMed 23631843) a confront\u00e9 Mifflin-St Jeor \u00e0 la calorim\u00e9trie indirecte de r\u00e9f\u00e9rence&nbsp;: 87&nbsp;% de pr\u00e9cision chez les sujets non ob\u00e8ses, mais seulement 68&nbsp;% chez les sujets ob\u00e8ses, avec des \u00e9carts atteignant 330&nbsp;kcal par jour.<\/p>\n\n  <p style=\"margin-bottom:8px\"><strong>Worked example.<\/strong> Woman at 1.65m, 85&nbsp;kg, 38&nbsp;% bodyfat&nbsp;:<\/p>\n\n  <div class=\"fig\">\n    <div class=\"fig-head\"><span class=\"l\">Figure 1<\/span><span class=\"r\">kcal<\/span><\/div>\n    <div class=\"fig-body\"><div class=\"cv-wrap\"><canvas id=\"chartBMR\" aria-label=\"BMR comparison: Mifflin-St Jeor 1670 kcal vs Lean patented proprietary model 1340 kcal, 330 kcal gap\"><\/canvas><\/div><\/div>\n    <p class=\"fig-cap\"><strong>Estimated BMR<\/strong> for a woman at 1.65m, 85&nbsp;kg, 38&nbsp;% bodyfat. The patented proprietary Lean model accounts for lean mass. Mifflin-St Jeor (Noom by default, with no lean-mass option), doesn&rsquo;t. A 330&nbsp;kcal gap, the equivalent of a full light meal.<\/p>\n  <\/div>\n\n  <p>330&nbsp;kcal d&rsquo;erreur, c&rsquo;est la diff\u00e9rence entre un d\u00e9ficit qui fonctionne et un plateau inexpliqu\u00e9. Aucun accompagnement comportemental, aussi bon soit-il, ne rattrape un objectif calorique faux d\u00e8s le d\u00e9part&nbsp;: il te rendra simplement plus assidu sur la mauvaise cible.<\/p>\n\n  <div class=\"bodyscan-illust\" style=\"margin:40px auto 8px;display:flex;flex-direction:column;align-items:center;gap:14px;max-width:200px\">\n    <div class=\"mini-phone\" style=\"max-width:200px\"><div class=\"notch\"><\/div><div class=\"scr\"><img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-bodyscan-result.webp\" alt=\"BodyScan IA Lean : bodyfat mesur\u00e9 par photo en 5 secondes\" width=\"1179\" height=\"2556\" loading=\"lazy\" decoding=\"async\" \/><\/div><\/div>\n    <div class=\"mini-cap\">Real body fat<strong>Photo, 5 seconds<\/strong><\/div>\n  <\/div>\n\n  <div class=\"statement\">\n    <div class=\"num\">400 kcal<\/div>\n    <div class=\"lbl\">gap between two women at 75&nbsp;kg, one at 22&nbsp;% bodyfat (BMR 1,650), the other at 38&nbsp;% (BMR 1,250). Noom gives them the same number, with no lean-mass option.<\/div>\n  <\/div>\n\n  <p>La conclusion est arithm\u00e9tique&nbsp;: une app qui ne conna\u00eet que ton poids, ta taille, ton \u00e2ge et ton sexe ne peut pas individualiser ton m\u00e9tabolisme. Il lui manque la variable qui compte.<\/p>\n<\/section>\n\n<section aria-labelledby=\"p2\" class=\"rev\">\n  <div class=\"section-label\"><span class=\"bar\"><\/span><span class=\"num\">02 &middot; Problem 2<\/span><\/div>\n  <h2 id=\"p2\">The activity factor, picked once and for all<\/h2>\n  <p>C&rsquo;est le maillon que le coaching ne peut pas compenser.<\/p>\n  <p>Une fois le m\u00e9tabolisme de base estim\u00e9, Noom doit en d\u00e9duire ta d\u00e9pense totale&nbsp;: le BMR plus les pas, les activit\u00e9s du quotidien, les s\u00e9ances et la digestion.<\/p>\n  <p>La m\u00e9thode tient en une question du questionnaire d&rsquo;inscription&nbsp;: choisis ton niveau d&rsquo;activit\u00e9 dans une liste. Ce coefficient s&rsquo;appelle PAL, pour Physical Activity Level.<\/p>\n  <ul>\n    <li>Sedentary (PAL 1.25): desk job, little walking<\/li>\n    <li>Lightly active (PAL 1.4): occasional walking, little sport<\/li>\n    <li>Active (PAL 1.6): regular walking, sport 3 to 5 times per week<\/li>\n    <li>Very active (PAL 1.8): intense sport almost daily or physical work<\/li>\n  <\/ul>\n  <p>Le BMR est ensuite multipli\u00e9 par ce nombre. C&rsquo;est tout le m\u00e9canisme derri\u00e8re ton objectif quotidien&nbsp;: une case coch\u00e9e le premier jour, jamais rediscut\u00e9e ensuite.<\/p>\n  <p>L&rsquo;approximation est grossi\u00e8re. Entre un dimanche sur le canap\u00e9 et une journ\u00e9e debout \u00e0 marcher, l&rsquo;\u00e9cart r\u00e9el d\u00e9passe largement ce qu&rsquo;un coefficient unique peut repr\u00e9senter.<\/p>\n  <p>Noom se synchronise correctement avec Apple Health et Google Fit et r\u00e9cup\u00e8re tes pas. Un bonus calorique peut s&rsquo;ajouter quand une s\u00e9ance est d\u00e9tect\u00e9e. Mais le socle du calcul reste le multiplicateur choisi \u00e0 l&rsquo;inscription.<\/p>\n\n  <div class=\"fig\">\n    <div class=\"fig-head\"><span class=\"l\">Figure 2 &middot; 7 real days<\/span><span class=\"r\">kcal\/day<\/span><\/div>\n    <div class=\"fig-body\"><div class=\"cv-wrap\"><canvas id=\"chartNEAT\" aria-label=\"Daily variability of caloric expenditure over 7 days, vs 2000 kcal fixed per Noom\"><\/canvas><\/div><\/div>\n    <p class=\"fig-cap\"><strong>Real expenditure<\/strong> measured over 7&nbsp;days for a Lean user. The grey line is what Noom was showing (2,000&nbsp;kcal flat, PAL Active \u00d7 BMR). The pink annotations show why each day moves.<\/p>\n  <\/div>\n\n  <p>Ton activit\u00e9 ne tient pas dans une case. Tu peux \u00eatre tr\u00e8s actif la semaine o\u00f9 tu encha\u00eenes les d\u00e9placements, et s\u00e9dentaire celle o\u00f9 tu travailles \u00e0 distance.<\/p>\n  <p>Quelle case cocher, alors&nbsp;? Aucune n&rsquo;est juste, et le TDEE affich\u00e9 reste durablement d\u00e9cal\u00e9 du r\u00e9el.<\/p>\n  <p>C&rsquo;est le point central&nbsp;: m\u00eame avec une \u00e9quation de m\u00e9tabolisme moderne, un PAL statique suffit \u00e0 fausser l&rsquo;ensemble. On ne d\u00e9duit pas le NEAT, l&rsquo;EAT et le TEF d&rsquo;un multiplicateur unique.<\/p>\n  <p>Un m\u00e9tabolisme estim\u00e9 sans mesure de composition corporelle, plus une d\u00e9pense d&rsquo;activit\u00e9 approxim\u00e9e par un coefficient fig\u00e9&nbsp;: les chances que l&rsquo;objectif final soit juste sont faibles.<\/p>\n\n  <div class=\"cta-band rev\">\n    <div class=\"l\">See your real TDEE, broken down into BMR + NEAT + EAT + TEF. Free download.<\/div>\n    <div class=\"stores\">\n      <a href=\"https:\/\/apps.apple.com\/fr\/app\/lean-calorie-ai-podometre\/id6738668646\" target=\"_blank\" rel=\"noopener\"><img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-appstore-official.webp\" alt=\"App Store\" width=\"413\" height=\"122\" loading=\"lazy\" decoding=\"async\" \/><\/a>\n      <a href=\"https:\/\/play.google.com\/store\/apps\/details?id=com.lean.testsqflite\" target=\"_blank\" rel=\"noopener\"><img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-googleplay-official.webp\" alt=\"Google Play\" width=\"315\" height=\"95\" loading=\"lazy\" decoding=\"async\" \/><\/a>\n    <\/div>\n  <\/div>\n<\/section>\n\n<section aria-labelledby=\"p3\" class=\"rev\">\n  <div class=\"section-label\"><span class=\"bar\"><\/span><span class=\"num\">03 &middot; Problem 3<\/span><\/div>\n  <h2 id=\"p3\">Metabolic adaptation, never modeled<\/h2>\n  <p>C&rsquo;est le point aveugle que ni les cours quotidiens ni les coachs ne couvrent.<\/p>\n  <p>En d\u00e9ficit prolong\u00e9, ton corps constate qu&rsquo;il re\u00e7oit moins d&rsquo;\u00e9nergie et r\u00e9duit sa consommation. Comme un t\u00e9l\u00e9phone qui passe en mode \u00e9conomie&nbsp;: tout continue de tourner, mais au ralenti.<\/p>\n  <p>C&rsquo;est l&rsquo;adaptation m\u00e9tabolique, et la litt\u00e9rature est constante&nbsp;: M\u00fcller 2015 (PubMed 26399868, r\u00e9analyse de l&rsquo;\u00e9tude du Minnesota), Doucet 2001 sur le d\u00e9ficit prolong\u00e9, Nunes 2020 (PMC7484122). Les fourchettes publi\u00e9es vont de 5 \u00e0 25&nbsp;% du m\u00e9tabolisme de base.<\/p>\n  <ul>\n    <li>Deficit of &minus;250&nbsp;kcal per day, over 2 to 8 weeks: adaptation of <strong>5 to 10%<\/strong> (TDEE drops to 90-95&nbsp;% of the initial level)<\/li>\n    <li>Deficit of &minus;500&nbsp;kcal per day: <strong>10 to 15%<\/strong> adaptation (TDEE drops to 85-90&nbsp;%)<\/li>\n    <li>Deficit of &minus;750&nbsp;kcal per day: <strong>15 to 25%<\/strong> adaptation (TDEE drops to 75-85&nbsp;%)<\/li>\n  <\/ul>\n  <p>Convention Lean&nbsp;: 100&nbsp;% signifie un m\u00e9tabolisme optimal, 90&nbsp;% une adaptation de 10&nbsp;%. Et comme le NEAT, l&rsquo;EAT et le TEF se calculent tous \u00e0 partir du BMR, c&rsquo;est l&rsquo;ensemble du TDEE qui se d\u00e9place.<\/p>\n\n  <div class=\"fig\">\n    <div class=\"fig-head\"><span class=\"l\">Figure 3 &middot; 8 weeks in deficit<\/span><span class=\"r\">kcal\/day<\/span><\/div>\n    <div class=\"fig-body\"><div class=\"cv-wrap\"><canvas id=\"chartAdapt\" aria-label=\"TDEE dropping from 2000 to 1720 kcal over 8 weeks, vs 2000 fixed per Noom\"><\/canvas><\/div><\/div>\n    <p class=\"fig-cap\"><strong>real TDEE<\/strong> over 8 weeks of deficit at &minus;500&nbsp;kcal\/day. The pink curve drops. The Noom line stays flat. By week 6, you&rsquo;re already at maintenance. Without having changed anything.<\/p>\n  <\/div>\n\n  <p>Un exemple chiffr\u00e9&nbsp;: tu vises un d\u00e9ficit de 25&nbsp;% sur un TDEE de 2&nbsp;000&nbsp;kcal, donc 1&nbsp;500&nbsp;kcal par jour. Ton corps s&rsquo;adapte de 14&nbsp;%, ton TDEE r\u00e9el tombe \u00e0 1&nbsp;720. Il ne te reste que 220&nbsp;kcal de d\u00e9ficit&nbsp;: la perte s&rsquo;arr\u00eate, sans que tu aies chang\u00e9 quoi que ce soit.<\/p>\n  <p>Ce qui rend le ph\u00e9nom\u00e8ne redoutable, c&rsquo;est sa lenteur. Les premi\u00e8res semaines fonctionnent, tu es confiant, tu continues. L&rsquo;adaptation se cumule en silence jusqu&rsquo;au jour o\u00f9 la balance se fige.<\/p>\n  <p>C&rsquo;est pr\u00e9cis\u00e9ment le moment o\u00f9 un accompagnement comportemental se retourne contre toi. Le coach t&rsquo;expliquera que le plateau est normal, qu&rsquo;il faut pers\u00e9v\u00e9rer, revoir tes habitudes. Alors que le probl\u00e8me n&rsquo;est ni ta discipline ni ta motivation&nbsp;: c&rsquo;est le chiffre cible qui a boug\u00e9, et personne ne l&rsquo;a mesur\u00e9.<\/p>\n  <p>Noom ne mod\u00e9lise pas ce ph\u00e9nom\u00e8ne. Ton objectif calorique reste fig\u00e9 tant que tu ne mets pas \u00e0 jour ton poids \u00e0 la main. Tu peux suivre tous les cours et classer chaque aliment&nbsp;: si la cible est fausse, la m\u00e9thode ne peut pas te rattraper.<\/p>\n<\/section>\n\n<section aria-labelledby=\"solution\" class=\"rev\">\n  <div class=\"section-label\"><span class=\"bar\"><\/span><span class=\"num\">04 &middot; Lean's solution<\/span><\/div>\n  <h2 id=\"solution\">How Lean fixes each of the 3 problems<\/h2>\n  <p>Noom et Lean ne jouent pas sur le m\u00eame terrain. Noom mise sur la psychologie du comportement&nbsp;: cours quotidiens, coachs humains, classification des aliments. C&rsquo;est coh\u00e9rent, et pour certains profils c&rsquo;est exactement ce qu&rsquo;il faut. Mais un accompagnement comportemental pos\u00e9 sur un objectif calorique faux reste un accompagnement vers la mauvaise cible. Lean travaille l&rsquo;autre moiti\u00e9 du probl\u00e8me&nbsp;: rendre ce chiffre juste, en mesurant chaque composant du TDEE (BMR&nbsp;+&nbsp;NEAT&nbsp;+&nbsp;EAT&nbsp;+&nbsp;TEF) plus l&rsquo;adaptation m\u00e9tabolique. Voici comment.<\/p>\n\n  <div class=\"method\">\n    <div class=\"m-phone\">\n      <div class=\"duo-row\">\n        <div>\n          <div class=\"mini-phone\"><div class=\"notch\"><\/div><div class=\"scr\"><img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-bodyscan-result.webp\" alt=\"R\u00e9sultat BodyScan IA : pourcentage de masse grasse mesur\u00e9 par photo\" width=\"1179\" height=\"2556\" loading=\"lazy\" decoding=\"async\" \/><\/div><\/div>\n          <div class=\"mini-cap\">Step 1<strong>AI BodyScan<\/strong><\/div>\n        <\/div>\n        <div>\n          <div class=\"mini-phone\"><div class=\"notch\"><\/div><div class=\"scr\"><img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-screen_BMR.webp\" alt=\"\u00c9cran BMR Lean : m\u00e9tabolisme de base calcul\u00e9 sur la masse maigre\" width=\"1179\" height=\"2556\" loading=\"lazy\" decoding=\"async\" \/><\/div><\/div>\n          <div class=\"mini-cap\">Step 2<strong>BMR recalculated<\/strong><\/div>\n        <\/div>\n      <\/div>\n    <\/div>\n    <div>\n      <div class=\"m-tag\">BMR on real bodyfat<\/div>\n      <h3>Proprietary patented model, built on lean mass<\/h3>\n      <p>Le coaching de Noom part d&rsquo;un objectif calorique calcul\u00e9 sur ton poids. Lean part de ta <strong>lean mass<\/strong>, parce que c&rsquo;est elle qui consomme au repos&nbsp;: \u00e0 poids identique, deux personnes n&rsquo;ont pas le m\u00eame m\u00e9tabolisme. Encore faut-il conna\u00eetre son taux de masse grasse sans passer par un DEXA en clinique.<\/p>\n      <p>D&rsquo;o\u00f9 le <strong>AI BodyScan<\/strong>&nbsp;: une photo, analys\u00e9e par un mod\u00e8le entra\u00een\u00e9 sur une banque de scans DEXA, et ton bodyfat s&rsquo;affiche en quelques secondes. Refait chaque semaine, il met \u00e0 jour ton m\u00e9tabolisme automatiquement. Aucun coach humain ne peut produire cette mesure \u00e0 cette fr\u00e9quence.<\/p>\n      <p>Exit la pince \u00e0 pli cutan\u00e9, la balance \u00e0 imp\u00e9dance et ses \u00e9carts selon l&rsquo;hydratation, le DEXA et son prix. Une photo par semaine suffit.<\/p>\n    <\/div>\n  <\/div>\n\n  <div class=\"method flip\">\n    <div>\n      <div class=\"m-tag\">No activity coefficient<\/div>\n      <h3>NEAT, EAT, TEF calculated separately<\/h3>\n      <p><strong>NEAT.<\/strong> Tes pas r\u00e9els arrivent via HealthKit (iOS) ou Google Fit (Android). L\u00e0 o\u00f9 un programme de coaching te demande de d\u00e9crire ton niveau d&rsquo;activit\u00e9, Lean lit les acc\u00e9l\u00e9rom\u00e8tres de ton t\u00e9l\u00e9phone et convertit ces pas en calories selon ton m\u00e9tabolisme. La diff\u00e9rence entre une journ\u00e9e \u00e0 4&nbsp;000 pas et une \u00e0 14&nbsp;000 se voit imm\u00e9diatement dans ton objectif du jour.<\/p>\n      <p><strong>EAT.<\/strong> Tu choisis ton sport et Lean applique le MET correspondant \u00e0 ton temps d&rsquo;effort r\u00e9el. Une heure de musculation avec ses temps de repos ne co\u00fbte pas une heure de course continue&nbsp;: les compter pareil fausse le bilan de plusieurs centaines de kcal par semaine.<\/p>\n      <p><strong>TEF.<\/strong> La digestion consomme de l&rsquo;\u00e9nergie, et pas au m\u00eame tarif selon les macros&nbsp;: 20 \u00e0 30&nbsp;% pour les prot\u00e9ines, 5 \u00e0 10&nbsp;% pour les glucides, 1 \u00e0 3&nbsp;% pour les lipides. Lean calcule ce poste sur ce que tu as r\u00e9ellement mang\u00e9, au lieu du forfait de 10&nbsp;% appliqu\u00e9 partout ailleurs.<\/p>\n    <\/div>\n    <div class=\"m-phone\">\n      <div class=\"mini-row\" style=\"margin:0;gap:10px\">\n        <div>\n          <div class=\"mini-phone tiny\"><div class=\"notch\"><\/div><div class=\"scr\"><img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-screen_NEAT.webp\" alt=\"\u00c9cran NEAT Lean\" width=\"1179\" height=\"2556\" loading=\"lazy\" decoding=\"async\" \/><\/div><\/div>\n          <div class=\"mini-cap\" style=\"font-size:10px\"><strong style=\"font-size:12px\">NEAT<\/strong><\/div>\n        <\/div>\n        <div>\n          <div class=\"mini-phone tiny\"><div class=\"notch\"><\/div><div class=\"scr\"><img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-screen_EAT.webp\" alt=\"\u00c9cran EAT Lean\" width=\"1179\" height=\"2556\" loading=\"lazy\" decoding=\"async\" \/><\/div><\/div>\n          <div class=\"mini-cap\" style=\"font-size:10px\"><strong style=\"font-size:12px\">EAT<\/strong><\/div>\n        <\/div>\n        <div>\n          <div class=\"mini-phone tiny\"><div class=\"notch\"><\/div><div class=\"scr\"><img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-screen_TEF.webp\" alt=\"\u00c9cran TEF Lean\" width=\"1179\" height=\"2556\" loading=\"lazy\" decoding=\"async\" \/><\/div><\/div>\n          <div class=\"mini-cap\" style=\"font-size:10px\"><strong style=\"font-size:12px\">TEF<\/strong><\/div>\n        <\/div>\n      <\/div>\n    <\/div>\n  <\/div>\n\n  <div class=\"method\">\n    <div class=\"m-phone\">\n      <div class=\"mini-phone\" style=\"max-width:170px\"><div class=\"notch\"><\/div><div class=\"scr\"><img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-screen_depense.webp\" alt=\"\u00c9cran d\u00e9pense totale Lean avec adaptation m\u00e9tabolique\" width=\"1179\" height=\"2556\" loading=\"lazy\" decoding=\"async\" \/><\/div><\/div>\n      <div class=\"mini-cap\">Method<strong>Auto adaptation<\/strong><\/div>\n    <\/div>\n    <div>\n      <div class=\"m-tag\">Automatic metabolic adaptation<\/div>\n      <h3>A world first on a consumer app<\/h3>\n      <p><strong>L&rsquo;adaptation m\u00e9tabolique.<\/strong> C&rsquo;est le point aveugle de tout programme comportemental&nbsp;: au bout de quelques semaines de d\u00e9ficit, ton m\u00e9tabolisme ralentit, et aucune motivation ne compense un objectif calorique devenu faux. Lean ajuste ton TDEE \u00e0 la baisse selon les fourchettes publi\u00e9es (M\u00fcller 2015, Doucet 2001), semaine apr\u00e8s semaine.<\/p>\n      <p>Au-del\u00e0 de 10 \u00e0 15&nbsp;% d&rsquo;adaptation, l&rsquo;app peut recommander un retour \u00e0 la maintenance pour relancer le m\u00e9tabolisme avant de repartir. C&rsquo;est ce que fait un pr\u00e9parateur, sauf que Lean le calcule sur tes donn\u00e9es.<\/p>\n      <p>Aucun niveau d&rsquo;activit\u00e9 \u00e0 d\u00e9clarer, aucune case coch\u00e9e une fois pour toutes. Chaque brique est mesur\u00e9e, chaque semaine.<\/p>\n    <\/div>\n  <\/div>\n<\/section>\n\n<section aria-labelledby=\"tab\" class=\"rev\">\n  <div class=\"section-label\"><span class=\"bar\"><\/span><span class=\"num\">05 &middot; Side-by-side<\/span><\/div>\n  <h2 id=\"tab\">Lean versus Noom, criterion by criterion<\/h2>\n  <p>An honest read of each app's strengths and weaknesses. No criterion touches price.<\/p>\n\n  <div class=\"table\" role=\"table\" aria-label=\"Lean vs Noom comparison\">\n    <div class=\"table-row head\" role=\"row\">\n      <div role=\"columnheader\">Criterion<\/div>\n      <div class=\"brand-cell lean\" role=\"columnheader\"><img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-logo-lean-square-scaled.webp\" alt=\"\" width=\"512\" height=\"512\" loading=\"lazy\" decoding=\"async\" \/> <span>Lean<\/span><\/div>\n      <div class=\"brand-cell\" role=\"columnheader\"><img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/logo-noom-real.webp\" alt=\"\" width=\"512\" height=\"512\" loading=\"lazy\" decoding=\"async\" \/> <span>Noom<\/span><\/div>\n    <\/div>\n    <div class=\"table-row\" role=\"row\">\n      <div class=\"crit\">BMR formula<\/div>\n      <div class=\"cell lean\"><span class=\"icn ok\"><svg viewbox=\"0 0 12 12\"><path d=\"M2 6.5 L5 9 L10 3.5\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg><\/span> Proprietary patented model (lean mass)<\/div>\n      <div class=\"cell\"><span class=\"icn no\"><svg viewbox=\"0 0 12 12\"><path d=\"M3 3 L9 9 M9 3 L3 9\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\"\/><\/svg><\/span> Mifflin-St Jeor 1990, no lean-mass option<\/div>\n    <\/div>\n    <div class=\"table-row\" role=\"row\">\n      <div class=\"crit\">Uses bodyfat<\/div>\n      <div class=\"cell lean\"><span class=\"icn ok\"><svg viewbox=\"0 0 12 12\"><path d=\"M2 6.5 L5 9 L10 3.5\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg><\/span> Yes, measured in the app<\/div>\n      <div class=\"cell\"><span class=\"icn no\"><svg viewbox=\"0 0 12 12\"><path d=\"M3 3 L9 9 M9 3 L3 9\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\"\/><\/svg><\/span> No, no bodyfat input possible<\/div>\n    <\/div>\n    <div class=\"table-row\" role=\"row\">\n      <div class=\"crit\">Bodyfat measured inside the app<\/div>\n      <div class=\"cell lean\"><span class=\"icn ok\"><svg viewbox=\"0 0 12 12\"><path d=\"M2 6.5 L5 9 L10 3.5\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg><\/span> BodyScan AI via photo<\/div>\n      <div class=\"cell\"><span class=\"icn no\"><svg viewbox=\"0 0 12 12\"><path d=\"M3 3 L9 9 M9 3 L3 9\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\"\/><\/svg><\/span> No<\/div>\n    <\/div>\n    <div class=\"table-row\" role=\"row\">\n      <div class=\"crit\">NEAT (steps, non-exercise activity)<\/div>\n      <div class=\"cell lean\"><span class=\"icn ok\"><svg viewbox=\"0 0 12 12\"><path d=\"M2 6.5 L5 9 L10 3.5\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg><\/span> Computed from real steps every day<\/div>\n      <div class=\"cell\"><span class=\"icn mid\">&minus;<\/span> HealthKit sync, exercise calorie add-on, but outside any TDEE recomputation<\/div>\n    <\/div>\n    <div class=\"table-row\" role=\"row\">\n      <div class=\"crit\">EAT (exercise expenditure)<\/div>\n      <div class=\"cell lean\"><span class=\"icn ok\"><svg viewbox=\"0 0 12 12\"><path d=\"M2 6.5 L5 9 L10 3.5\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg><\/span> Per sport via MET, effective time<\/div>\n      <div class=\"cell\"><span class=\"icn mid\">&minus;<\/span> Simple sport selection, standard exercise database<\/div>\n    <\/div>\n    <div class=\"table-row\" role=\"row\">\n      <div class=\"crit\">TEF (digestion)<\/div>\n      <div class=\"cell lean\"><span class=\"icn ok\"><svg viewbox=\"0 0 12 12\"><path d=\"M2 6.5 L5 9 L10 3.5\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg><\/span> Calculated from macros, integrated into the TDEE<\/div>\n      <div class=\"cell\"><span class=\"icn no\"><svg viewbox=\"0 0 12 12\"><path d=\"M3 3 L9 9 M9 3 L3 9\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\"\/><\/svg><\/span> No, macros displayed without TEF computation<\/div>\n    <\/div>\n    <div class=\"table-row\" role=\"row\">\n      <div class=\"crit\">Metabolic adaptation<\/div>\n      <div class=\"cell lean\"><span class=\"icn ok\"><svg viewbox=\"0 0 12 12\"><path d=\"M2 6.5 L5 9 L10 3.5\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg><\/span> Automatic, week by week<\/div>\n      <div class=\"cell\"><span class=\"icn no\"><svg viewbox=\"0 0 12 12\"><path d=\"M3 3 L9 9 M9 3 L3 9\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\"\/><\/svg><\/span> No<\/div>\n    <\/div>\n    <div class=\"table-row\" role=\"row\">\n      <div class=\"crit\">Activity multiplier to pick<\/div>\n      <div class=\"cell lean\"><span class=\"icn ok\"><svg viewbox=\"0 0 12 12\"><path d=\"M2 6.5 L5 9 L10 3.5\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg><\/span> No, computed on real data<\/div>\n      <div class=\"cell\"><span class=\"icn no\"><svg viewbox=\"0 0 12 12\"><path d=\"M3 3 L9 9 M9 3 L3 9\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\"\/><\/svg><\/span> Yes, static level chosen through the sign-up quiz<\/div>\n    <\/div>\n    <div class=\"table-row\" role=\"row\">\n      <div class=\"crit\">AI photo scan of a meal<\/div>\n      <div class=\"cell lean\"><span class=\"icn ok\"><svg viewbox=\"0 0 12 12\"><path d=\"M2 6.5 L5 9 L10 3.5\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg><\/span> Yes, unlimited<\/div>\n      <div class=\"cell\"><span class=\"icn no\"><svg viewbox=\"0 0 12 12\"><path d=\"M3 3 L9 9 M9 3 L3 9\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\"\/><\/svg><\/span> No, manual entry or barcode<\/div>\n    <\/div>\n    <div class=\"table-row\" role=\"row\">\n      <div class=\"crit\">Barcode scan<\/div>\n      <div class=\"cell lean\"><span class=\"icn ok\"><svg viewbox=\"0 0 12 12\"><path d=\"M2 6.5 L5 9 L10 3.5\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg><\/span> Yes<\/div>\n      <div class=\"cell\"><span class=\"icn ok\"><svg viewbox=\"0 0 12 12\"><path d=\"M2 6.5 L5 9 L10 3.5\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg><\/span> Yes<\/div>\n    <\/div>\n    <div class=\"table-row\" role=\"row\">\n      <div class=\"crit\">Food database<\/div>\n      <div class=\"cell lean\"><span class=\"icn ok\"><svg viewbox=\"0 0 12 12\"><path d=\"M2 6.5 L5 9 L10 3.5\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg><\/span> USDA + OpenFoodFacts, curated<\/div>\n      <div class=\"cell\"><span class=\"icn mid\">&minus;<\/span> Proprietary database + green\/yellow\/red classification<\/div>\n    <\/div>\n    <div class=\"table-row\" role=\"row\">\n      <div class=\"crit\">Calorie deficit recommendation<\/div>\n      <div class=\"cell lean\"><span class=\"icn ok\"><svg viewbox=\"0 0 12 12\"><path d=\"M2 6.5 L5 9 L10 3.5\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg><\/span> Adapted to real TDEE<\/div>\n      <div class=\"cell\"><span class=\"icn no\"><svg viewbox=\"0 0 12 12\"><path d=\"M3 3 L9 9 M9 3 L3 9\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\"\/><\/svg><\/span> Fixed target, manual recompute required<\/div>\n    <\/div>\n    <div class=\"table-row\" role=\"row\">\n      <div class=\"crit\">Human coaching and behavioral therapy<\/div>\n      <div class=\"cell lean\"><span class=\"icn no\"><svg viewbox=\"0 0 12 12\"><path d=\"M3 3 L9 9 M9 3 L3 9\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\"\/><\/svg><\/span> Out of scope, focus on TDEE computation<\/div>\n      <div class=\"cell\"><span class=\"icn ok\"><svg viewbox=\"0 0 12 12\"><path d=\"M2 6.5 L5 9 L10 3.5\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg><\/span> Human coaches, groups, daily lessons<\/div>\n    <\/div>\n    <div class=\"table-row\" role=\"row\">\n      <div class=\"crit\">Green\/yellow\/red food classification<\/div>\n      <div class=\"cell lean\"><span class=\"icn no\"><svg viewbox=\"0 0 12 12\"><path d=\"M3 3 L9 9 M9 3 L3 9\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\"\/><\/svg><\/span> Out of scope<\/div>\n      <div class=\"cell\"><span class=\"icn ok\"><svg viewbox=\"0 0 12 12\"><path d=\"M2 6.5 L5 9 L10 3.5\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg><\/span> Mainstream educational reference<\/div>\n    <\/div>\n    <div class=\"table-row\" role=\"row\">\n      <div class=\"crit\">EU coverage and localization<\/div>\n      <div class=\"cell lean\"><span class=\"icn ok\"><svg viewbox=\"0 0 12 12\"><path d=\"M2 6.5 L5 9 L10 3.5\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg><\/span> FR, EN, ES, PT, IT, DE, PL, HU<\/div>\n      <div class=\"cell\"><span class=\"icn ok\"><svg viewbox=\"0 0 12 12\"><path d=\"M2 6.5 L5 9 L10 3.5\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg><\/span> EN, ES, DE and others, founded in NYC 2008<\/div>\n    <\/div>\n    <div class=\"table-row\" role=\"row\">\n      <div class=\"crit\">Reputation and audience size<\/div>\n      <div class=\"cell lean\"><span class=\"icn ok\"><svg viewbox=\"0 0 12 12\"><path d=\"M2 6.5 L5 9 L10 3.5\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg><\/span> 4.7\/5, 10,000+ users, young FR app<\/div>\n      <div class=\"cell\"><span class=\"icn ok\"><svg viewbox=\"0 0 12 12\"><path d=\"M2 6.5 L5 9 L10 3.5\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg><\/span> 4.5\/5, 50M+ downloads, audience women 35-55<\/div>\n    <\/div>\n    <div class=\"table-row\" role=\"row\">\n      <div class=\"crit\">Business model<\/div>\n      <div class=\"cell lean\"><span class=\"icn ok\"><svg viewbox=\"0 0 12 12\"><path d=\"M2 6.5 L5 9 L10 3.5\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg><\/span> Premium, 7-day free trial on the annual subscription<\/div>\n      <div class=\"cell\"><span class=\"icn ok\"><svg viewbox=\"0 0 12 12\"><path d=\"M2 6.5 L5 9 L10 3.5\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg><\/span> Paid subscription, short trial period<\/div>\n    <\/div>\n  <\/div>\n<\/section>\n\n<section aria-labelledby=\"tracking\" class=\"rev\">\n  <div class=\"section-label\"><span class=\"bar\"><\/span><span class=\"num\">06 &middot; Tracking<\/span><\/div>\n  <h2 id=\"tracking\">3 ways to track a meal<\/h2>\n  <p>Un programme de coaching tient sur la dur\u00e9e si le geste quotidien est simple. C&rsquo;est exactement le pari de Lean sur le tracking&nbsp;: trois fa\u00e7ons d&rsquo;enregistrer un repas, pour qu&rsquo;aucune situation ne devienne une excuse pour abandonner.<\/p>\n\n  <div class=\"mini-row\">\n    <div>\n      <div class=\"mini-phone\"><div class=\"notch\"><\/div><div class=\"scr\"><img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-database.webp\" alt=\"Recherche dans la base de donn\u00e9es USDA + OpenFoodFacts\" width=\"1179\" height=\"2556\" loading=\"lazy\" decoding=\"async\" \/><\/div><\/div>\n      <div class=\"mini-cap\">Method 1<strong>Food database<\/strong><\/div>\n    <\/div>\n    <div>\n      <div class=\"mini-phone\"><div class=\"notch\"><\/div><div class=\"scr\"><img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-codebarre.webp\" alt=\"Scan de code-barres dans Lean\" width=\"1179\" height=\"2556\" loading=\"lazy\" decoding=\"async\" \/><\/div><\/div>\n      <div class=\"mini-cap\">Method 2<strong>Barcode<\/strong><\/div>\n    <\/div>\n    <div>\n      <div class=\"mini-phone\"><div class=\"notch\"><\/div><div class=\"scr\"><img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-scania.webp\" alt=\"Scan photo IA d'un plat\" width=\"1179\" height=\"2556\" loading=\"lazy\" decoding=\"async\" \/><\/div><\/div>\n      <div class=\"mini-cap\">Method 3<strong>AI photo scan<\/strong><\/div>\n    <\/div>\n  <\/div>\n\n  <ol>\n    <li><strong>Database search.<\/strong> Curated base, USDA + OpenFoodFacts. No community noise, no \"Roast chicken\" entered 47 times by 47 different users with 47 different values.<\/li>\n    <li><strong>Barcode scan.<\/strong> Standard. You scan your pasta box, you get the macros.<\/li>\n    <li><strong>AI photo scan of a meal.<\/strong> You take a photo of your plate, the AI detects the foods, you get calories and macros per food. Noom doesn&rsquo;t offer this feature.<\/li>\n  <\/ol>\n  <p>Le scan photo IA sert surtout quand tu manges dehors. Noom te demandera de qualifier ton plat par couleur&nbsp;; Lean te demande une photo, et tu passes \u00e0 autre chose. Sur un d\u00e9jeuner d&rsquo;affaires ou un d\u00eener chez des amis, la diff\u00e9rence de friction est d\u00e9cisive.<\/p>\n  <p>Au-dessus du repas, Lean affiche un TDEE qui bouge pendant la journ\u00e9e&nbsp;: plus tu marches, plus ton objectif calorique monte. Un programme hebdomadaire fixe ne peut pas restituer cette variation quotidienne.<\/p>\n  <p>Et pour hi\u00e9rarchiser ce qui compte vraiment, la Pyramide de Progression&nbsp;:<\/p>\n\n  <div class=\"pyramid\" aria-label=\"Lean Progression Pyramid\">\n    <div class=\"level l1\"><span>Adherence<\/span><span class=\"k\">Base<\/span><\/div>\n    <div class=\"level l2\"><span>Calorie target<\/span><span class=\"k\">Tier 2<\/span><\/div>\n    <div class=\"level l3\"><span>Steps \/ NEAT<\/span><span class=\"k\">Tier 3<\/span><\/div>\n    <div class=\"level l4\"><span>Macronutrients<\/span><span class=\"k\">Top<\/span><\/div>\n  <\/div>\n  <div class=\"pyramid-cap\">Don&rsquo;t skip steps. If you&rsquo;re not consistent on tracking, optimizing macros to the percent is pointless.<\/div>\n<\/section>\n\n<section aria-labelledby=\"noom-better\" class=\"rev\">\n  <div class=\"section-label\"><span class=\"bar\"><\/span><span class=\"num\">07 &middot; Honesty<\/span><\/div>\n  <h2 id=\"noom-better\">What Noom does better<\/h2>\n  <p>Lean is not perfect, and Noom has several real strengths worth acknowledging. Honest read, criterion by criterion, on the axes where Noom stays ahead. None of these axes is secondary: they are real pillars of the Noom promise, and they explain its massive adoption among the target audience of women aged 35 to 55 on the long-term weight loss topic.<\/p>\n\n  <div class=\"scorecard rev\" aria-label=\"Noom vs Lean scorecard across 4 axes: psychology and adherence\">\n    <div class=\"scorecard-head\">\n      <div class=\"h-crit\">Axis<\/div>\n      <div class=\"h-brand\"><img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/logo-noom-real.webp\" alt=\"\" width=\"512\" height=\"512\" loading=\"lazy\" decoding=\"async\" \/> Noom<\/div>\n      <div class=\"h-brand\"><img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-logo-lean-square-scaled.webp\" alt=\"\" width=\"512\" height=\"512\" loading=\"lazy\" decoding=\"async\" \/> Lean<\/div>\n    <\/div>\n    <div class=\"scorecard-row\">\n      <div class=\"crit\">Human coaching and behavioral therapy<\/div>\n      <div class=\"bar mfp\"><div class=\"b\"><i style=\"width:94%\"><\/i><\/div><div class=\"v\">9,4<\/div><\/div>\n      <div class=\"bar lean\"><div class=\"b\"><i style=\"width:20%\"><\/i><\/div><div class=\"v\">2,0<\/div><\/div>\n    <\/div>\n    <div class=\"scorecard-row\">\n      <div class=\"crit\">Daily food-psychology lessons<\/div>\n      <div class=\"bar mfp\"><div class=\"b\"><i style=\"width:92%\"><\/i><\/div><div class=\"v\">9,2<\/div><\/div>\n      <div class=\"bar lean\"><div class=\"b\"><i style=\"width:25%\"><\/i><\/div><div class=\"v\">2,5<\/div><\/div>\n    <\/div>\n    <div class=\"scorecard-row\">\n      <div class=\"crit\">Intuitive green\/yellow\/red classification<\/div>\n      <div class=\"bar mfp\"><div class=\"b\"><i style=\"width:88%\"><\/i><\/div><div class=\"v\">8,8<\/div><\/div>\n      <div class=\"bar lean\"><div class=\"b\"><i style=\"width:30%\"><\/i><\/div><div class=\"v\">3,0<\/div><\/div>\n    <\/div>\n    <div class=\"scorecard-row\">\n      <div class=\"crit\">Long-term adherence work<\/div>\n      <div class=\"bar mfp\"><div class=\"b\"><i style=\"width:90%\"><\/i><\/div><div class=\"v\">9,0<\/div><\/div>\n      <div class=\"bar lean\"><div class=\"b\"><i style=\"width:70%\"><\/i><\/div><div class=\"v\">7,0<\/div><\/div>\n    <\/div>\n  <\/div>\n\n  <p style=\"margin-top:30px\"><strong>Honest read.<\/strong> On human coaching, Noom is the mainstream reference: your coach chats with you, the support groups (Noom community) run continuously, and it&rsquo;s a real emotional accompaniment for those who need it. On the daily food-psychology lessons (5 to 10 minutes each day, inspired by CBT, cognitive behavioral therapy), Noom has invested massively and it&rsquo;s unique on the market: no other calorie tracker offers this structured educational content. On the green\/yellow\/red classification, it&rsquo;s an intuitive mechanism that saves the user time and works well for profiles who don&rsquo;t want to dive into macros. On long-term adherence, Chin 2016 and many meta-analyses on behavioral therapy applied to weight loss show significant gains at 6 and 12 months. Noom builds scientifically on that axis.<\/p>\n  <p>If your main angle is psychological work on food habits, if you need a human coach to hold on, or if the green\/yellow\/red classification helps you make choices without calculating, Noom is more relevant than Lean. If your angle is the precision of <a class=\"inline\" href=\"https:\/\/lean-app.com\/en\/tdee-calculator\/\">TDEE calculation<\/a>, bodyfat measured every week through BodyScan AI, and automatic metabolic adaptation, that&rsquo;s exactly what was demonstrated in the 3 previous sections. Many users run Lean for measurement and Noom in parallel for psychological coaching, which is entirely defensible.<\/p>\n<\/section>\n\n<section aria-labelledby=\"forwho\" class=\"rev\">\n  <div class=\"section-label\"><span class=\"bar\"><\/span><span class=\"num\">08 &middot; Who it's for<\/span><\/div>\n  <h2 id=\"forwho\">Who Lean is built for<\/h2>\n  <p>Quatre profils. Si l&rsquo;un d&rsquo;eux te correspond, Lean a des chances de te convenir.<\/p>\n\n  <div class=\"persona\">\n    <div class=\"persona-it match\">\n      <div class=\"pic\"><svg viewbox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2.4\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 12 L9 18 L21 5\"\/><\/svg><\/div>\n      <div>\n        <h4>You followed Noom seriously and didn&rsquo;t lose<\/h4>\n        <p>Tu as fait le quiz, suivi les cours, class\u00e9 tes repas par couleur, \u00e9chang\u00e9 avec ton coach, et tenu plusieurs semaines sans que la courbe suive vraiment.<\/p>\n      <\/div>\n    <\/div>\n    <div class=\"persona-it match\">\n      <div class=\"pic\"><svg viewbox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2.4\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 12 L9 18 L21 5\"\/><\/svg><\/div>\n      <div>\n        <h4>You plateau after several weeks of cutting<\/h4>\n        <p>Le plateau s&rsquo;installe apr\u00e8s quatre \u00e0 huit semaines. C&rsquo;est la signature de l&rsquo;adaptation m\u00e9tabolique&nbsp;: Lean la calcule et corrige ton objectif au lieu de te renvoyer \u00e0 ta motivation.<\/p>\n      <\/div>\n    <\/div>\n    <div class=\"persona-it match\">\n      <div class=\"pic\"><svg viewbox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2.4\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 12 L9 18 L21 5\"\/><\/svg><\/div>\n      <div>\n        <h4>You want to understand your metabolism<\/h4>\n        <p>Tu veux voir le d\u00e9tail du calcul&nbsp;: BMR, NEAT, EAT et TEF affich\u00e9s s\u00e9par\u00e9ment, adaptation expliqu\u00e9e \u00e0 part, plut\u00f4t qu&rsquo;un chiffre unique comment\u00e9 par un coach.<\/p>\n      <\/div>\n    <\/div>\n    <div class=\"persona-it match\">\n      <div class=\"pic\"><svg viewbox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2.4\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 12 L9 18 L21 5\"\/><\/svg><\/div>\n      <div>\n        <h4>You want tracking that lasts 12 months<\/h4>\n        <p>Tu manges souvent dehors et tu as besoin d&rsquo;un enregistrement rapide&nbsp;: photo, base cur\u00e9e ou code-barres selon le contexte.<\/p>\n      <\/div>\n    <\/div>\n  <\/div>\n\n  <p style=\"margin-top:30px\"><strong>Noom stays more relevant for<\/strong>&nbsp;: working psychologically on food habits, enjoying a human coach and support groups, following daily lessons of cognitive behavioral therapy applied to weight loss, or relying on the green\/yellow\/red classification to make choices without calculating. Precision of TDEE calculation and metabolic adaptation just aren&rsquo;t part of its main promise.<\/p>\n<\/section>\n\n<section aria-labelledby=\"migrate\" class=\"rev\">\n  <div class=\"section-label\"><span class=\"bar\"><\/span><span class=\"num\">09 &middot; Migration<\/span><\/div>\n  <h2 id=\"migrate\">Switching from Noom to Lean (or using both) in 3 minutes<\/h2>\n\n  <div class=\"steps\">\n    <div class=\"step\"><div class=\"sn\">01<\/div><h4>Download Lean<\/h4><p>App Store ou Play Store. Inscription en trente secondes, sans questionnaire de vingt minutes.<\/p><\/div>\n    <div class=\"step\"><div class=\"sn\">02<\/div><h4>AI BodyScan<\/h4><p>Une photo, cinq secondes&nbsp;: ton taux de masse grasse s&rsquo;affiche.<\/p><\/div>\n    <div class=\"step\"><div class=\"sn\">03<\/div><h4>Weight &amp; height<\/h4><p>Tu saisis ton poids et ta taille. Rien d&rsquo;autre n&rsquo;est demand\u00e9.<\/p><\/div>\n    <div class=\"step\"><div class=\"sn\">04<\/div><h4>Lean calculates<\/h4><p>BMR sur masse maigre r\u00e9elle, NEAT depuis tes pas via HealthKit ou Google Fit, EAT par MET, TEF sur tes macros, et l&rsquo;adaptation m\u00e9tabolique qui module le tout semaine apr\u00e8s semaine.<\/p><\/div>\n    <div class=\"step\"><div class=\"sn\">05<\/div><h4>Log a meal<\/h4><p>Photo, code-barres ou base de donn\u00e9es&nbsp;: tu choisis selon le repas.<\/p><\/div>\n  <\/div>\n\n  <p style=\"margin-top:24px\"><strong>Important note.<\/strong> Lean doesn&rsquo;t import your Noom history automatically, nor your exchanges with your human coach. If you appreciate Noom&rsquo;s psychological coaching and daily lessons, many users keep using Noom for behavioral work and support groups, while using Lean daily for the TDEE calculation and precise tracking. The HealthKit \/ Google Health Connect sync, on the other hand, takes over immediately for your steps and activity history.<\/p>\n\n  <div class=\"cta-band rev\">\n    <div class=\"l\">Download Lean and start the BodyScan AI right now. Free sign-up.<\/div>\n    <div class=\"stores\">\n      <a href=\"https:\/\/apps.apple.com\/fr\/app\/lean-calorie-ai-podometre\/id6738668646\" target=\"_blank\" rel=\"noopener\"><img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-appstore-official.webp\" alt=\"App Store\" width=\"413\" height=\"122\" loading=\"lazy\" decoding=\"async\" \/><\/a>\n      <a href=\"https:\/\/play.google.com\/store\/apps\/details?id=com.lean.testsqflite\" target=\"_blank\" rel=\"noopener\"><img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-googleplay-official.webp\" alt=\"Google Play\" width=\"315\" height=\"95\" loading=\"lazy\" decoding=\"async\" \/><\/a>\n    <\/div>\n  <\/div>\n<\/section>\n\n<section aria-labelledby=\"deblock-h\" class=\"rev\">\n  <div class=\"section-label\"><span class=\"bar\"><\/span><span class=\"num\">10 &middot; What Lean unlocks<\/span><\/div>\n  <h2 id=\"deblock-h\">What Lean does, that Noom doesn&rsquo;t (on TDEE)<\/h2>\n  <p>Six fonctionnalit\u00e9s absentes des trackers grand public. Elles reposent toutes sur le m\u00eame choix&nbsp;: mesurer chaque composant du TDEE plut\u00f4t que l&rsquo;estimer, puis l&rsquo;habiller de p\u00e9dagogie.<\/p>\n\n  <div class=\"feat-stack\">\n    <div class=\"feat-it\"><div class=\"fn\">01<\/div><div><div class=\"ft\">Unlimited BodyScan AI<\/div><p class=\"fd\">Ton taux de masse grasse, obtenu depuis une photo et actualis\u00e9 chaque semaine. C&rsquo;est la variable qui rend le m\u00e9tabolisme individuel, et aucun programme de coaching ne la mesure.<\/p><\/div><div class=\"fc\">Body fat<\/div><\/div>\n    <div class=\"feat-it\"><div class=\"fn\">02<\/div><div><div class=\"ft\">Unlimited AI photo scan of a meal<\/div><p class=\"fd\">Un repas au restaurant enregistr\u00e9 en deux secondes, sans balance ni saisie. La friction en moins qui fait tenir sur douze mois, sans avoir besoin qu&rsquo;un coach te relance.<\/p><\/div><div class=\"fc\">Adherence<\/div><\/div>\n    <div class=\"feat-it\"><div class=\"fn\">03<\/div><div><div class=\"ft\">Automatic metabolic adaptation<\/div><p class=\"fd\">Ton TDEE se corrige semaine apr\u00e8s semaine selon les fourchettes publi\u00e9es. Les plateaux qu&rsquo;un accompagnement attribue \u00e0 la motivation trouvent ici leur explication chiffr\u00e9e.<\/p><\/div><div class=\"fc\">Adaptation<\/div><\/div>\n    <div class=\"feat-it\"><div class=\"fn\">04<\/div><div><div class=\"ft\">Live TDEE breakdown<\/div><p class=\"fd\">BMR, NEAT, EAT et TEF affich\u00e9s s\u00e9par\u00e9ment et mis \u00e0 jour dans la journ\u00e9e. Ton objectif bouge avec ton activit\u00e9 r\u00e9elle, au lieu d&rsquo;\u00eatre arr\u00eat\u00e9 au r\u00e9veil.<\/p><\/div><div class=\"fc\">Live<\/div><\/div>\n    <div class=\"feat-it\"><div class=\"fn\">05<\/div><div><div class=\"ft\">Full history and trends<\/div><p class=\"fd\">Tes tendances de poids, de masse grasse et de masse maigre sur plusieurs mois. Tu identifies tes cycles au lieu de r\u00e9agir \u00e0 la pes\u00e9e du jour.<\/p><\/div><div class=\"fc\">History<\/div><\/div>\n    <div class=\"feat-it\"><div class=\"fn\">06<\/div><div><div class=\"ft\">3 unified tracking methods<\/div><p class=\"fd\">Une hi\u00e9rarchie claire de ce qui compte&nbsp;: l&rsquo;adh\u00e9rence d&rsquo;abord, puis l&rsquo;objectif calorique, puis les pas. De quoi savoir quoi ajuster quand \u00e7a bloque.<\/p><\/div><div class=\"fc\">Tracking<\/div><\/div>\n  <\/div>\n\n  <p style=\"margin-top:26px\">You install the app for free, you test without commitment, then you decide whether the tool fits your goal.<\/p>\n<\/section>\n\n<section aria-labelledby=\"faq-h\" class=\"rev\">\n  <div class=\"section-label\"><span class=\"bar\"><\/span><span class=\"num\">11 &middot; FAQ<\/span><\/div>\n  <h2 id=\"faq-h\">Frequently asked questions<\/h2>\n  <div class=\"faq\">\n    <details><summary>Noom is known for its psychological coaching, why compare it to Lean on TDEE&nbsp;?<\/summary><div class=\"ans\">Noom is renowned for its behavioral approach (daily CBT-inspired lessons, human coaches, green\/yellow\/red food classification). That&rsquo;s a real strength for adherence and psychological work on eating. But the underlying calorie engine remains Mifflin-St Jeor 1990 without bodyfat, plus a static activity factor chosen through the sign-up quiz. On TDEE, the engine is basic. Lean recalculates your BMR every day on your real bodyfat measured by BodyScan AI, and modulates through metabolic adaptation. The two apps don&rsquo;t play on the same field.<\/div><\/details>\n    <details><summary>Why doesn&rsquo;t Noom calculate the BMR on real bodyfat&nbsp;?<\/summary><div class=\"ans\">Noom applies Mifflin-St Jeor 1990 by default without a lean-mass option. No bodyfat measurement is built into the app, and no Katch-McArdle-type equation is offered even in advanced settings. The consequence is mechanical: two users of the same weight but with 22 and 38 percent bodyfat get the same Noom BMR, while their real expenditure can differ by 400 kcal per day. Lean integrates BodyScan AI to measure your bodyfat from a simple photo, to redo every week.<\/div><\/details>\n    <details><summary>Is Noom&rsquo;s green\/yellow\/red classification a real metabolic measure&nbsp;?<\/summary><div class=\"ans\">No. The green\/yellow\/red system classifies foods by calorie density (vegetables in green, starches in yellow, fats and sugars in red). It&rsquo;s an educational behavioral tool, not a measurement of energy expenditure. It&rsquo;s effective for raising awareness about choices, but it doesn&rsquo;t influence the TDEE calculation. On your real metabolism, Noom stays on Mifflin 1990 plus a few static activity boxes. The color classification doesn&rsquo;t modify the calculated calorie target.<\/div><\/details>\n    <details><summary>Noom imports steps via HealthKit, is that enough for NEAT&nbsp;?<\/summary><div class=\"ans\">Noom imports steps and activity through Apple Health and Google Fit, but uses them to estimate an exercise expenditure added to the daily calorie target. The static activity factor picked at the sign-up quiz remains the base of the TDEE calculation. Lean, on the contrary, calculates NEAT directly from real steps measured every day, with no coefficient to pick.<\/div><\/details>\n    <details><summary>Does Noom&rsquo;s human coaching replace a precise TDEE calculation&nbsp;?<\/summary><div class=\"ans\">Noom&rsquo;s human coaching is a real added value for adherence and psychological work on habits. Chin 2016 and many meta-analyses show that behavioral therapy improves weight loss at 6 and 12 months. But coaching doesn&rsquo;t act on the underlying TDEE equation. If your calorie target is calculated on Mifflin 1990 without bodyfat and a static PAL, your coach won&rsquo;t fix the equation, he&rsquo;ll encourage you to hold a potentially wrong deficit. Coaching is an adherence multiplier, not a substitute for objective measurement.<\/div><\/details>\n    <details><summary>Can you use Lean and Noom in parallel&nbsp;?<\/summary><div class=\"ans\">Yes, it&rsquo;s defensible. If you appreciate Noom&rsquo;s human coaching, daily lessons, and behavioral pedagogy, you can keep Noom for the psychological and habits side. Lean takes care of the precise metabolic engine (BMR on real bodyfat, NEAT, EAT, TEF, adaptation). The databases are different (USDA + OpenFoodFacts on the Lean side, proprietary database on the Noom side) so the double-entry effort is real: it&rsquo;s a trade-off to arbitrate based on your priorities.<\/div><\/details>\n  <\/div>\n<\/section>\n\n<section aria-labelledby=\"conclu\" class=\"rev\">\n  <div class=\"section-label\"><span class=\"bar\"><\/span><span class=\"num\">12 &middot; Conclusion<\/span><\/div>\n  <h2 id=\"conclu\">Coaching versus measurement<\/h2>\n  <p>It&rsquo;s not Noom versus Lean in marketing. It&rsquo;s psychological coaching versus metabolic precision, two different promises.<\/p>\n  <p>Noom remains one of the best mainstream apps for behavioral work on eating, and nobody in the mainstream does better on daily food-psychology lessons and human-coach accompaniment. But for your TDEE, Noom uses Mifflin-St Jeor 1990 without bodyfat measured inside the app, plus a frozen activity factor you tick once during the sign-up quiz, and ignores metabolic adaptation. The combination of all three makes any precise calorie tracking impossible beyond a few weeks of cut. It&rsquo;s mathematics. No human coach corrects an equation he doesn&rsquo;t see.<\/p>\n  <p>Lean was built to do the exact opposite: BMR based on <a class=\"inline\" href=\"https:\/\/lean-app.com\/en\/depense-energetique-totale-v2\/\">real bodyfat<\/a> (measured by BodyScan AI) via a proprietary patented model, <a class=\"inline\" href=\"https:\/\/lean-app.com\/en\/neat-depense-non-sportive\/\">NEAT from real steps<\/a>, EAT per sport via MET, <a class=\"inline\" href=\"https:\/\/lean-app.com\/en\/effet-thermique-des-aliments\/\">TEF from macros<\/a>, plus metabolic adaptation that modulates the BMR week after week. Each component calculated precisely, no magic coefficient, no psychological wrapping.<\/p>\n  <p>Noom remains very solid on behavioral coaching and human accompaniment. The best results often come from combining the two: measuring right (Lean) AND acting with discipline (sometimes helped by a Noom coach). If you tried Noom seriously and didn&rsquo;t get the results you hoped for on your cut, the problem isn&rsquo;t you, nor Noom on its psychological promise. The problem is the TDEE frozen under the hood. Change the engine, keep the coach alongside if you need it.<\/p>\n<\/section>\n\n<div class=\"get-band rev\">\n  <div class=\"kicker\">Download<\/div>\n  <h3>Lean is available as a free download<\/h3>\n  <p>iOS and Android. The BodyScan AI works from a single photo. No skinfold calliper, no bioimpedance scale, no DEXA.<\/p>\n  <div class=\"stores\">\n    <a href=\"https:\/\/apps.apple.com\/fr\/app\/lean-calorie-ai-podometre\/id6738668646\" target=\"_blank\" rel=\"noopener\" aria-label=\"Download Lean on the App Store\">\n      <img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-appstore-official.webp\" alt=\"App Store\" width=\"413\" height=\"122\" loading=\"lazy\" decoding=\"async\" \/>\n    <\/a>\n    <a href=\"https:\/\/play.google.com\/store\/apps\/details?id=com.lean.testsqflite\" target=\"_blank\" rel=\"noopener\" aria-label=\"Download Lean on Google Play\">\n      <img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-googleplay-official.webp\" alt=\"Google Play\" width=\"315\" height=\"95\" loading=\"lazy\" decoding=\"async\" \/>\n    <\/a>\n  <\/div>\n<\/div>\n\n<section aria-labelledby=\"links\">\n  <div class=\"section-label\"><span class=\"bar\"><\/span><span class=\"num\">Further reading<\/span><\/div>\n  <h3 id=\"links\" style=\"margin-top:0\">Internal links<\/h3>\n  <ul>\n    <li><a class=\"inline\" href=\"https:\/\/lean-app.com\/en\/tdee-calculator\/\">Free online TDEE calculator<\/a> &middot; web version, no sign-up, same logic as the app (BMR + NEAT + EAT + TEF).<\/li>\n    <li><a class=\"inline\" href=\"https:\/\/lean-app.com\/en\/depense-energetique-totale-v2\/\">Understand TDEE in depth (BMR, NEAT, EAT, TEF, adaptation)<\/a> &middot; deep-science article.<\/li>\n    <li><a class=\"inline\" href=\"https:\/\/lean-app.com\/en\/comment-compter-ses-calories\/\">How to count your calories properly<\/a> &middot; practical guide for beginners.<\/li>\n    <li><a class=\"inline\" href=\"https:\/\/lean-app.com\/en\/neat-depense-non-sportive\/\">NEAT: expenditure from steps and non-exercise activity<\/a>.<\/li>\n    <li><a class=\"inline\" href=\"https:\/\/lean-app.com\/en\/effet-thermique-des-aliments\/\">TEF: digestion burns calories<\/a>.<\/li>\n  <\/ul>\n<\/section>\n\n<section aria-labelledby=\"src\" class=\"sources\">\n  <div class=\"section-label\"><span class=\"bar\"><\/span><span class=\"num\">Sources<\/span><\/div>\n  <h3 id=\"src\" style=\"margin-top:0;color:var(--ink)\">References<\/h3>\n  <ol>\n    <li>Harris J.A., Benedict F.G. (1919). A Biometric Study of Basal Metabolism in Man. Carnegie Institution of Washington.<\/li>\n    <li>Mifflin M.D. et al. (1990). A new predictive equation for resting energy expenditure in healthy individuals. American Journal of Clinical Nutrition.<\/li>\n    <li>Katch V.L., McArdle W.D. (1973). Prediction of body density from simple anthropometric measurements in college-age men and women. Human Biology.<\/li>\n    <li>Chin S.O. et al. (2016). Successful weight reduction and maintenance by using a smartphone application in those with overweight and obesity. Scientific Reports, behavioral therapy and weight loss.<\/li>\n    <li>Frankenfield D.C. et al. (2013). Validation of Mifflin-St Jeor equation in obese and non-obese populations. PubMed 23631843.<\/li>\n    <li>M&uuml;ller M.J., Bosy-Westphal A. (2015). Adaptive thermogenesis with weight loss in humans. Obesity, Minnesota revisit. PubMed 26399868.<\/li>\n    <li>Doucet E. et al. (2001). Evidence for the existence of adaptive thermogenesis during weight loss. British Journal of Nutrition.<\/li>\n    <li>Westerterp K.R. (2004). Diet induced thermogenesis. Nutrition and Metabolism.<\/li>\n  <\/ol>\n<\/section>\n\n<\/main>\n\n<footer>\n  <div class=\"wrap\">\n    <div class=\"row\">\n      <div>\n        <div class=\"kicker\">Lean &middot; lean-app.com<\/div>\n        <p>Article published on May 24, 2026. Updated regularly with user feedback and relevant new studies. Lean is available on iOS and Android.<\/p>\n      <\/div>\n      <div class=\"stores\">\n        <a href=\"https:\/\/apps.apple.com\/fr\/app\/lean-calorie-ai-podometre\/id6738668646\" target=\"_blank\" rel=\"noopener\"><img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-appstore-official.webp\" alt=\"App Store\" width=\"413\" height=\"122\" loading=\"lazy\" decoding=\"async\" \/><\/a>\n        <a href=\"https:\/\/play.google.com\/store\/apps\/details?id=com.lean.testsqflite\" target=\"_blank\" rel=\"noopener\"><img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-googleplay-official.webp\" alt=\"Google Play\" width=\"315\" height=\"95\" loading=\"lazy\" decoding=\"async\" \/><\/a>\n      <\/div>\n    <\/div>\n  <\/div>\n<\/footer>\n\n<script>\n(function(){\n  var bar = document.getElementById('progBar');\n  function up(){\n    var h = document.documentElement;\n    var sc = (h.scrollTop)\/Math.max(1,(h.scrollHeight - h.clientHeight));\n    bar.style.transform = 'scaleX(' + Math.max(0,Math.min(1,sc)) + ')';\n  }\n  document.addEventListener('scroll', up, {passive:true});\n  up();\n})();\n\n(function(){\n  if (!('IntersectionObserver' in window)) {\n    document.querySelectorAll('.rev').forEach(function(n){n.classList.add('on')});\n    return;\n  }\n  var obs = new IntersectionObserver(function(entries){\n    entries.forEach(function(e){\n      if (e.isIntersecting) { e.target.classList.add('on'); obs.unobserve(e.target); }\n    });\n  }, {threshold:0.12});\n  document.querySelectorAll('.rev').forEach(function(n){ obs.observe(n); });\n})();\n\n(function(){\n  var phoneImg = document.getElementById('phoneImg');\n  var phoneBack = document.getElementById('phoneBack');\n  var zones = document.getElementById('phoneZones');\n  var topTabs = document.querySelectorAll('.phone-tabs button');\n  var navTaps = document.querySelectorAll('.phone-navbar button');\n\n  var tabMap = {\n    bilan:    {src:'https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-screen_bilan.webp',     drill:false},\n    kcal:     {src:'https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-screen_kcal.webp',      drill:false},\n    depense:  {src:'https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-screen_depense.webp',   drill:true},\n    strategie:{src:'https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-screen_strategie.webp', drill:false}\n  };\n  var subMap = {\n    BMR:  'https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-screen_BMR.webp',\n    NEAT: 'https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-screen_NEAT.webp',\n    EAT:  'https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-screen_EAT.webp',\n    TEF:  'https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-screen_TEF.webp'\n  };\n  var currentTab = 'depense';\n\n  function setActive(tab){\n    topTabs.forEach(function(b){ b.classList.toggle('on', b.dataset.tab===tab); });\n  }\n  function showTab(tab){\n    var t = tabMap[tab]; if(!t) return;\n    currentTab = tab;\n    phoneImg.style.opacity = 0;\n    setTimeout(function(){\n      phoneImg.className = 'phone-bg tab-' + tab;\n      phoneImg.style.opacity = 1;\n      zones.style.display = t.drill ? 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-->\n<aside class=\"lean-mesh\" style=\"margin:48px auto;max-width:760px;padding:24px 28px;background:#ffffff;border-left:4px solid #FF2D6E;border-radius:0 12px 12px 0;box-shadow:0 6px 24px rgba(20,20,40,0.06);font-family:-apple-system,'SF Pro Text','Segoe UI',Roboto,Arial,sans-serif;color:#1a1a2e;\"><p style=\"margin:0 0 14px;font-size:13px;font-weight:700;letter-spacing:0.06em;text-transform:uppercase;color:#FF2D6E;\">Read also<\/p><ul style=\"list-style:none;padding:0;margin:0;display:grid;grid-template-columns:1fr;gap:10px;\"><li><a href=\"\/en\/metabolisme-de-base\/\" style=\"display:block;padding:14px 16px;background:#FAF7F2;border-radius:8px;color:#1a1a2e;text-decoration:none;font-weight:600;line-height:1.4;\">Basal Metabolic Rate (BMR): everything you need to know to calculate it accurately <span style=\"color:#4D4D52;font-weight:400;display:block;font-size:14px;margin-top:4px;\">Definition, TDEE equation, 4 historical formulas, why bodyfat changes everything.<\/span><\/a><\/li><li><a href=\"\/en\/depense-energetique-totale-v2\/\" style=\"display:block;padding:14px 16px;background:#FAF7F2;border-radius:8px;color:#1a1a2e;text-decoration:none;font-weight:600;line-height:1.4;\">Total Daily Energy Expenditure (TDEE): the canonical formula BMR + NEAT + EAT + TEF <span style=\"color:#4D4D52;font-weight:400;display:block;font-size:14px;margin-top:4px;\">Understand the 4 components + metabolic adaptation, scientific sources 2025.<\/span><\/a><\/li><li><a href=\"\/en\/eat\/\" style=\"display:block;padding:14px 16px;background:#FAF7F2;border-radius:8px;color:#1a1a2e;text-decoration:none;font-weight:600;line-height:1.4;\">EAT: your real workout expenditure, session by session <span style=\"color:#4D4D52;font-weight:400;display:block;font-size:14px;margin-top:4px;\">Correct MET values, the double-counting trap, what Garmin and MyFitnessPal miss.<\/span><\/a><\/li><li><a href=\"\/en\/meilleures-applications-calories-2026\/\" style=\"display:block;padding:14px 16px;background:#FAF7F2;border-radius:8px;color:#1a1a2e;text-decoration:none;font-weight:600;line-height:1.4;\">Best calorie counting apps in 2026: 8 apps tested <span style=\"color:#4D4D52;font-weight:400;display:block;font-size:14px;margin-top:4px;\">Lean, MFP, Cronometer, Yazio, Lifesum, FatSecret, Noom, Foodvisor.<\/span><\/a><\/li><li><a href=\"\/en\/alternative-myfitnesspal\/\" style=\"display:block;padding:14px 16px;background:#FAF7F2;border-radius:8px;color:#1a1a2e;text-decoration:none;font-weight:600;line-height:1.4;\">Which MyFitnessPal alternative in 2026? 5 apps tested <span style=\"color:#4D4D52;font-weight:400;display:block;font-size:14px;margin-top:4px;\">Honest comparison, TDEE accuracy, usability.<\/span><\/a><\/li><li><a href=\"\/en\/comparatifs\/\" style=\"display:block;padding:14px 16px;background:#FAF7F2;border-radius:8px;color:#1a1a2e;text-decoration:none;font-weight:600;line-height:1.4;\">All Lean comparisons against major calorie apps <span style=\"color:#4D4D52;font-weight:400;display:block;font-size:14px;margin-top:4px;\">Hub: MyFitnessPal, Yazio, Cronometer, Lifesum, FatSecret, Noom.<\/span><\/a><\/li><li><a href=\"\/en\/lean-vs-myfitnesspal\/\" style=\"display:block;padding:14px 16px;background:#FAF7F2;border-radius:8px;color:#1a1a2e;text-decoration:none;font-weight:600;line-height:1.4;\">Lean vs MyFitnessPal: the TDEE formula that changes everything <span style=\"color:#4D4D52;font-weight:400;display:block;font-size:14px;margin-top:4px;\">Why MFP gets your real calorie expenditure wrong.<\/span><\/a><\/li><li><a href=\"\/en\/calculateur-tdee\/\" style=\"display:block;padding:14px 16px;background:#FAF7F2;border-radius:8px;color:#1a1a2e;text-decoration:none;font-weight:600;line-height:1.4;\">TDEE Calculator: the canonical formula BMR + NEAT + EAT + TEF <span style=\"color:#4D4D52;font-weight:400;display:block;font-size:14px;margin-top:4px;\">Bodyfat-aware calculator with breakdown of the 4 metabolic components.<\/span><\/a><\/li><\/ul><\/aside>","protected":false},"excerpt":{"rendered":"<p>Lean TDEE Calculator Home &nbsp;\/&nbsp; Lean vs Noom Comparison &middot; Nutrition &amp; TDEE Lean vs Noom. Psychological coaching vs metabolic precision. Noom sells behavioral coaching to change your habits. Lean sees your real expenditure. Two promises that do not play on the same field. The Lean team &middot; Read 12&nbsp;min &middot; Updated [&hellip;]<\/p>","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"single-lvm-blank","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-1425","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v25.6 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Lean vs Noom: psychological coaching vs metabolic precision - Lean<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/lean-app.com\/en\/lean-vs-noom\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Lean vs Noom: psychological coaching vs metabolic precision - Lean\" \/>\n<meta property=\"og:description\" content=\"Lean TDEE Calculator Home &nbsp;\/&nbsp; Lean vs Noom Comparison &middot; Nutrition &amp; TDEE Lean vs Noom. Psychological coaching vs metabolic precision. Noom sells behavioral coaching to change your habits. Lean sees your real expenditure. Two promises that do not play on the same field. 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